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Stream/Area:
-
Subject:
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Paper ID:
IJSREM63911
Volume:
10 |
5 |
May 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

P. G. Department of Computer Science
Pro. Ram Meghe Institute of Technology and Research

P. G. Department of Computer Science
Pro. Ram Meghe Institute of Technology and Research
P. G. Department of Computer Science
Pro. Ram Meghe Institute of Technology and Research
Machine learning (ML) has become a transformative force across multiple domains, enabling automated decision-making, predictive analytics, and intelligent data processing. This review explores the broad spectrum of ML applications, including supervised, unsupervised, and reinforcement learning, across industries such as healthcare, finance, cybersecurity, education, transportation, agriculture, and energy. By analyzing recent advancements, different ML techniques address domain specific challenges and enhance operational efficiency. Additionally, this work provides insights for researchers and practitioners seeking to harness ML for real-world applications.
KEYWORDS
Machine Learning has become a powerful technology that is transforming various sectors such as healthcare, finance, agriculture, education, transportation, and cybersecurity. By enabling intelligent automation, prediction, and data-driven decision-making, ML improves efficiency, accuracy, and productivity across industries. This study highlights the different types of Machine Learning and their real-world applications in multiple domains. Although ML offers significant advantages, challenges such as data privacy, bias, and high computational cost still need to be addressed. With continuous advancements in Artificial Intelligence and data technologies, Machine Learning is expected to play an even greater role in developing smart, secure, and efficient systems in the future.
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Publication Date
5/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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