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Machine Learning (ML) Market to Reach USD 225.91 Billion by 2030; Integration of Machine Intelligence with Analytics Solutions to Boost Market Progress

May 05, 2023 | Information & Technology

The global Machine Learning (ML) market size touched USD 19.20 billion in 2022 and is anticipated to be valued at USD 26.03 billion in 2023. The market share is predicted to reach USD 225.91 billion by 2030, recording a CAGR of 36.2% over 2023-2030.

Fortune Business Insights™ presents this information in its latest report titled “Machine Learning (ML) Market Size, Share & COVID-19 Impact Analysis, By Enterprise Type (Small & Mid-sized Enterprises (SMEs) and Large Enterprises), By Deployment (Cloud and On-premise), By End-Use Industry (Healthcare, Retail, IT and Telecommunication, BFSI, Automotive and Transportation, Advertising and Media, Manufacturing, and Others), and Regional Forecast, 2023-2030”.

Machine Learning (ML) is a data analytics technology that can train computers to learn from data and algorithms so that they can imitate the human way of learning. Artificial Intelligence Market (AI) and ML technologies are finding robust usage in a wide range of end-use industries such as healthcare, automotive, retail, and manufacturing, among others. Furthermore, the COVID-19 pandemic prompted governments across the world to impose various movement restrictions and social distancing norms. Many countries were also adopting these measures to minimize the impact of the COVID-19 pandemic.

Key Market Players Find Diverse Growth Opportunities During COVID-19 Pandemic

The COVID-19 outbreak was instrumental in accelerating the Machine Learning market growth, and this trend may continue in the future as well. ML technology is being extensively used in several industries, including automotive, retail, and healthcare.

AI technology was of immense help to researchers who were looking for various ways to combat this pandemic. For example, according to the Brookings Institution, South Korean researchers obtained geolocation data and surveillance camera footage to keep a close track of patients infected by coronavirus. These data scientists used the video footage and machine intelligence algorithms to anticipate the location of the next outbreak and inform the appropriate authorities to track COVID-19 positive patients in real-time. These developments may spur the usage of ML solutions in the coming years.

Amazon SageMaker to Introduce Novel Capabilities to Boost Machine Learning Performance

In December 2022, Amazon SageMaker, the end-to-end ML service, offered by Amazon Web Services (AWS), announced that it introduced eight brand-new capabilities. These capabilities will help improve the performance of ML processes. Amazon SageMaker’s governance capabilities offered model performance visibility throughout the lifecycle of machine learning.

To get a detailed report summary and research scope of this market, click here:


ML finds Robust Applications in the Healthcare Sector

ML technology has become quite popular in several healthcare applications. This industry vertical uses ML to analyze millions of diverse data points and predict outcomes. It also provides quick risk scores and exact resource allocation.

One of the most critical applications of this technology in healthcare is detecting and diagnosing conditions/illnesses that cannot be identified in their early stages. This can include various hereditary diseases and cancers. For instance, IBM Watson Genomics, which shows how a combination of genome-based tumor sequencing and cognitive computing, can help medical professionals boost their cancer diagnosis capabilities, is a prominent example of the importance of ML in healthcare. Berg, a biopharmaceutical giant, utilizes AI to produce therapeutic solutions for areas such as cancer.

Competitive Landscape

Key Players to Introduce New Functionalities in their Existing Offerings to Strengthen Market Position

The top companies are providing automated machine intelligence solutions to ease the process of making learning models and speed up the average time to market. For example, Microsoft’s Machine Learning Operations (MLOps) or DevOps uses Azure’s ML capabilities. The tech behemoth is regularly introducing new capabilities in its product offerings to fortify its market presence.

Notable Industry Development:

  • April 2021: Microsoft Corporation introduced an open database for transportation, health & genomics, population & safety, labor & economics, and other areas to bolster the accuracy of ML models that use datasets that are publicly available. This enables the company to deliver hyperscale insights by using Azure Open Datasets in combination with Azure's data analytics and ML solutions, thereby increasing ML-as-a-Service (MLaaS) sales.

List of the Companies Profiled in the Report:

  • IBM Corporation (U.S.)

  • SAP SE (Germany)

  • Oracle Corporation (U.S.)

  • Hewlett Packard Enterprise Company (U.S.)

  • Microsoft Corporation (U.S.)

  • Amazon, Inc. (U.S.)

  • Intel Corporation (U.S.)

  • Databricks (U.S.)

  • SAS Institute Inc. (U.S.)

  • BigML, Inc. (U.S.)

Further Report Findings

  • Europe is expected to register strong growth rate in the global market due to the rising use of ML technology in emerging countries that have a large base of highly skilled workers such as Germany and the U.K.

  • Large enterprises segment accounted for the largest market share in 2022. These organizations are implementing AI technology and data science on a large scale to get quantitative insights into their operations. Large-scale companies are also trying to harness artificial learning, deep learning, and optimization of decisions to provide quality services to their clients.

Table of Segmentation



Study Period


Base Year


Estimated Year


Forecast Period


Historical Period


Growth Rate

CAGR of 36.2% from 2023 to 2030


Value (USD billion)


By Enterprise Type, Deployment, End-use Industry, and Region

By Enterprise Type

  • Small and Mid-sized Enterprises (SMEs)

  • Large Enterprises

By Deployment

  • Cloud

  • On-premise

By End-use Industry

  • Healthcare

  • Retail

  • IT and Telecommunication

  • Banking, Financial Services and Insurance (BFSI)

  • Automotive & Transportation

  • Advertising & Media

  • Manufacturing

  • Others (Energy & Utilities)

By Region

  • North America (By Enterprise Type, By Deployment, By End-use Industry, By Country)

    • U.S.

    • Canada

  • Europe (By Enterprise Type, By Deployment, By End-use Industry, By Country)

    • U.K.

    • Germany

    • France

    • Scandinavia

    • Rest of Europe

  • Asia Pacific (By Enterprise Type, By Deployment, By End-use Industry, By Country)

    • China

    • Japan

    • India

    • Southeast Asia

    • Rest of Asia Pacific

  • Middle East & Africa (By Enterprise Type, By Deployment, By End-use Industry, By Country)

    • GCC

    • South Africa

    • Rest of the Middle East & Africa

  • Latin America (By Enterprise Type, By Deployment, By End-use Industry, By Country)

    • Brazil

    • Mexico

    • Rest of Latin America

Global Machine Learning Market
  • PDF
  • 2022
  • 2019-2021
  • 160


  • 4850

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