Healthy longevity has emerged as a major goal of modern healthcare, shifting the emphasis from extending lifespan to maximizing healthspan. Although lifestyle interventions effectively prevent chronic diseases and promote healthy ageing, selecting the most appropriate intervention for individual patients remains challenging because of biological heterogeneity, incomplete clinical information, and multiple sources of uncertainty. Existing computational approaches, including artificial intelligence, machine learning, mathematical modeling, and operations research, have shown considerable potential for personalized healthcare; however, they provide limited guidance on selecting the most appropriate method for specific clinical decision-making contexts. This narrative review proposes a decision-oriented conceptual framework for selecting mathematical modeling approaches to support healthy longevity. Evidence from healthy longevity, Precision Lifestyle Medicine, artificial intelligence, mathematical modeling, and operations research was synthesized to classify computational methods according to clinical objectives, dominant sources of uncertainty, and data characteristics. The proposed framework links specific healthcare scenarios with appropriate approaches, including machine learning, Bayesian networks, grey system theory, fuzzy decision models, multi-objective optimization, and digital twins. Unlike previous studies that mainly compare algorithmic performance, the proposed framework emphasizes clinical applicability and decision suitability. It provides practical guidance for selecting computational methods to support personalized lifestyle interventions, improve healthspan, and facilitate the development of intelligent clinical decision-support systems for precision healthcare.