Machine Learning (ML) continues to evolve rapidly, with revolutionary advancements emerging daily. Its pervasive impact extends across multiple sectors, including healthcare, finance, security, and business management, where it extracts value from previously untapped data reservoirs. Despite the burgeoning growth, there remains a significant void in the literature that concisely details the principles of ML algorithms and offers assistance for optimal model selection tailored for a given scenario. This paper fills this gap by providing an exhaustive review of supervised and unsupervised ML models. Through an in-depth analysis encompassing algorithmic intricacies, applications, strengths, weaknesses, ideal-case scenarios, pitfalls, essential preprocessing requirements, and suitability (SWiPES), this research provides an understanding of the intricate landscape of ML models. A critical contribution is the provision of strategic visualizations that succinctly encapsulate the SWiPES of each model, aiding in the swift identification of the most fitting model for a given real-world scenario. Furthermore, this paper provides a practical blueprint, a compass for researchers, practitioners, and ML enthusiasts, facilitating them to make informed decisions about the most appropriate model based on specific problem domains and dataset characteristics. This research intends to be the definitive ML review, unlocking the potential for precision and insight in navigating the complicated landscape of machine learning.
Keywords: Machine Learning, Strategic Guide, Unsupervised Learning, Supervised Learning, Best-fit Models
How to cite this article: Faisal A, Jhanjhi NZ, Ashraf H, Ray SK, Ashfaq F, Alhawi OA, Khan A., A Comprehensive Review of Machine Learning Models: Principles, Applications, and Optimal Model Selection. Int J Drug Deliv Technol. 2026;16(2s): 1-32; DOI: 10.25258/ijddt.16.1-32
Source of support: Nil.
Conflict of interest: None