Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{188472,
        author = {Rutuja Madhukar Kale and Komal Sanjay Shinde and Payal Prakash Bansod and Akanksha Siddharth Nagdeve and Rutuja Shivaji Wawre and Prachi Dhondiba Waghmare},
        title = {Emerging Horizons in Machine Learning: A Comprehensive Review of Contemporary Trends and Evolving Paradigms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {2009-2026},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188472},
        abstract = {Machine learning has undergone a period of transformative growth between 2019 and 2025, driven by rapid advancements in model architectures, scalable learning paradigms, and cross-disciplinary integration. This review examines contemporary trends that have reshaped the field, including the rise of transformer-based models, progress in self-supervised and multimodal learning, expansion of graph neural networks, and breakthroughs in generative modelling through diffusion frameworks. Additionally, emerging priorities such as federated and privacy-preserving learning, explainable AI, robustness under distribution shifts, and sustainable model design are analyzed to highlight evolving research motivations and real-world constraints. A systematic methodology is employed to identify, classify, and synthesize findings from recent high-impact studies, enabling a thematic understanding of technological innovation and its implications. Comparative performance evaluations reveal significant trade-offs between accuracy, computational cost, data efficiency, and interpretability, emphasizing that no single technique universally dominates across metrics. The review concludes by outlining key challenges and proposing future research directions that stress efficiency, transparency, ethics, and human AI collaboration. Overall, this survey provides a comprehensive overview of the shifting landscape of machine learning and offers insights that are essential for guiding subsequent research and development.},
        keywords = {Machine Learning, Deep Learning, Transformers, Self-Supervised Learning, Multimodal Learning, Generative Models, Diffusion Models, Graph Neural Networks (GNNs), Sustainable AI, Generalist AI Models, Human AI Collaboration, Eerging Trends in AI},
        month = {December},
        }