• G. Vijay kumar

The convergence of Machine Learning (ML) and Deep Learning (DL) has significantly influenced the development of intelligent systems capable of solving complex real-world problems. This paper presents a comprehensive study on the integration of ML and DL techniques across various domains such as healthcare, agriculture, transportation, and cybersecurity. It emphasizes the comparative performance of traditional ML models, standalone DL architectures, and hybrid models that leverage the strengths of both. By analyzing case studies and evaluating performance using key metrics, this research highlights the advantages of hybrid models in terms of accuracy, robustness, and scalability. The study also discusses current challenges, identifies gaps in existing research, and suggests future directions for developing efficient, interpretable, and domain-adaptable AI systems.

Machine Learning, Deep Learning, Artificial Intelligence, Real-World Applications, Hybrid Models, Predictive Analyticsq

"Integrating Machine Learning and Deep Learning Techniques for Real-World Applications: A Comprehensive Study Across Diverse Domains", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.259-263, May-2025, Available :http://www.jetir.org/papers/JETIRGV06037.pdf

"Integrating Machine Learning and Deep Learning Techniques for Real-World Applications: A Comprehensive Study Across Diverse Domains", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. pp259-263, May-2025, Available at : http://www.jetir.org/papers/JETIRGV06037.pdf