• Mrs. Namrata Wankhade
• Prof. Snehal Andhare

Diabetes is a common metabolic illness that may be dangerous if undetected. Effective illness treatment and complication avoidance need early and precise identification. With advances in artificial intelligence, machine learning (ML) may analyze medical data and uncover patterns that conventional diagnostic approaches may miss to improve diabetes diagnosis. This review article examines diabetes prediction using ML approaches including supervised and unsupervised learning algorithms, deep learning models, and ensemble methods. It discusses popular datasets, feature selection methods, model performance measures, and ML-based diagnostic system implementation issues. The report also covers limits, biases, and future research to improve ML diabetes diagnosis reliability and practical usefulness. The results show that ML might revolutionize diabetes screening and diagnosis, making healthcare more efficient and accessible.

Diabetes detection, machine learning, artificial intelligence, predictive modeling, supervised learning, deep learning, feature selection, classification algorithms, healthcare analytics, Etc.

"Enhancing Diabetes Diagnosis with Machine Learning: A Review of Algorithms and Applications ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 3, page no.c52-c59, March-2026, Available :http://www.jetir.org/papers/JETIR2603209.pdf

"Enhancing Diabetes Diagnosis with Machine Learning: A Review of Algorithms and Applications ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 3, page no. ppc52-c59, March-2026, Available at : http://www.jetir.org/papers/JETIR2603209.pdf