Diabetes management is often guided by generalized diet plans, which are time-consuming, inflexible,
and ineffective for personalized care. Managing multiple lifestyle factors such as physical activity, sleep
duration, and stress level further complicates nutritional planning when conventional methods are used.
This paper presents a NextGen AI Framework for Adaptive Lifestyle and Nutrition Analytics in Diabetic
Care to automate personalized dietary recommendations. The proposed system predicts individualized
macronutrient requirements by analyzing lifestyle inputs through machine learning techniques and maps
the predicted values to Indian food datasets to provide culturally relevant recommendations. In addition,
the framework delivers real-time visualization through an interactive dashboard, thereby improving
usability and interpretability. The proposed approach reduces dependence on static meal plans, enhances
personalization, and offers an efficient and reliable solution for modern diabetes nutrition management.
