Advancements in Machine Learning: A Comprehensive Review of Algorithms, Applications, and Future Directions

Year : 2025 | Volume : 12 | Issue : 02 | Page : 17 33
    By

    Kshitish Mule,

  • Dheeraj Malviya,

  • Vishwajeet Goswami,

  • Suyog Gharat,

  • Kartik Patil,

  • Kamlesh Pawar,

  • Vaibhavi Lahare,

  1. Student, Department of Computer Science and Engineering, School of Engineering, Ajeenkya D Y Patil University, Charholi Bk, Pune, Maharashtra, India
  2. Student, Department of Computer Science and Engineering, School of Engineering, Ajeenkya D Y Patil University, Charholi Bk, Pune, Maharashtra, India
  3. Professor, Department of Computer Science and Engineering, School of Engineering, Ajeenkya D Y Patil University, Charholi Bk, Pune, Maharashtra, India
  4. Student, Department of Computer Science and Engineering, School of Engineering, Ajeenkya D Y Patil University, Charholi Bk, Pune, Maharashtra, India
  5. Student, Department of Computer Science and Engineering, School of Engineering, Ajeenkya D Y Patil University, Charholi Bk, Pune, Maharashtra, India
  6. Student, Department of Computer Science and Engineering, School of Engineering, Ajeenkya D Y Patil University, Charholi Bk, Pune, Maharashtra, India
  7. Student, Department of Computer Science and Engineering, School of Engineering, Ajeenkya D Y Patil University, Charholi Bk, Pune, Maharashtra, India

Abstract

Gaining knowledge of Machine learning (ML)-guided format algorithms leverage predictive models to generate novel devices with optimized properties across several domains, which include drug discovery, fabric synthesis, and biomolecular engineering. Selecting an effective format set of policies consists of identifying appropriate hyperparameters, predictive models, and generative mechanisms to maximize format fulfilment. This study introduces an established method for set of policies requirements, ensuring that generated designs meet predefined fulfilment criteria, which includes accomplishing a minimum proportion of high-performing designs. By integrating predicted property values with held-out categorised records, the proposed method estimates label distributions throughout each type of format strategy, drawing from concept in prediction-powered inference. The method is theoretically confident to select format algorithms that yield preferred outcomes, provided that accurate density ratios some of the generated and categorised records distributions. To validate the effectiveness of this framework, we apply it to simulated protein and RNA format tasks, demonstrating its software in every identified and expected density ratio scenario. Additionally, we provide a whole assessment of recent enhancements in artificial intelligence (AI) and ML, highlighting key gaining knowledge of paradigms, neural networks, and generative models. Emerging trends, which include ethical AI, explainability, and AI`s integration with location computing and the Internet of Things (IoT), are explored alongside annoying conditions related to records privacy, model interpretability, and computational sustainability. By synthesizing insights from contemporary research, this study offers a holistic mindset on ML-driven format, supplying guidance on set of policies desired and future hints in AI-powered innovation.

Keywords: Machine learning, predictive modelling, algorithm, hypothesis testing, design optimization

[This article belongs to Recent Trends in Programming languages ]

How to cite this article:
Kshitish Mule, Dheeraj Malviya, Vishwajeet Goswami, Suyog Gharat, Kartik Patil, Kamlesh Pawar, Vaibhavi Lahare. Advancements in Machine Learning: A Comprehensive Review of Algorithms, Applications, and Future Directions. Recent Trends in Programming languages. 2025; 12(02):17-33.
How to cite this URL:
Kshitish Mule, Dheeraj Malviya, Vishwajeet Goswami, Suyog Gharat, Kartik Patil, Kamlesh Pawar, Vaibhavi Lahare. Advancements in Machine Learning: A Comprehensive Review of Algorithms, Applications, and Future Directions. Recent Trends in Programming languages. 2025; 12(02):17-33. Available from: https://journals.stmjournals.com/rtpl/article=2025/view=217720


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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 11/04/2025
Accepted 07/05/2025
Published 13/06/2025
Publication Time 63 Days
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