Predictive Analytics Framework Using Machine Learning for Personalized Nutrition and Lifestyle Recommendations: A Technical Approach toward Women’s Wellness

Abstract

The use of artificial intelligence, machine learning and predictive analytics in nutrition and lifestyle studies has expanded rapidly. However, many of the studies that are currently available are not sufficiently addressing women-specific health issues. The purpose of this study is to examine and compile the most current studies on AI and machine learning-based methods for evaluating women's nutrition and lifestyle. A comprehensive review of published research was carried out, with particular focus on studies that used machine learning methods for nutrition and health analysis, including Random Forest, Decision Tree, Support Vector Machine, K- Nearest Neighbours, and Naive Bayes. Less than 20% of the examined 30 peer-reviewed publications address individualized lifestyle or nutrition guidance systems, specifically for women, whereas more than 70% concentrate on obesity prediction or nutrient deficiency detection. These findings suggest a significant research gap in integrated predictive analytics frameworks that are women-centric. In order to create more individualized and data-driven nutrition and lifestyle solutions for women's health, this review offers recommendations for future research on AI- and ML-based blended framework represents a meaningful step toward personalized wellness recommendations. Tailoring dietary and lifestyle guidance specifically to women is a promising direction that aligns with modern trends in precision health.