Conventional weight loss programs often apply generalized nutritional guidelines, ignoring individual variations in metabolism and dietary response. Such limitations result in inconsistent progress and reduced long-term success rates. This research presents a data-driven framework to enhance personalized weight management by analyzing protein intake patterns using machine learning and deep learning models. The study employs data from the MyFitnessPal dataset and user-provided records, including diet logs, anthropometric parameters, and demographic details. Pre-processing is performed using a Target Encoding Wavelet Transform Filter (TEWTF) to remove noise and normalize heterogeneous data. Advanced algorithms such as Random Forest Generative Adversarial Networks (RFGAN) and Preference-Aware Inverse Optimization (PAIO) are implemented to create adaptive diet plans aligned with user preferences. Furthermore, Hierarchical Clustering combined with Multi-Layer Perceptron (HCMLP) is utilized to identify protein consumption trends and predict optimal intake levels for diverse user groups. The model dynamically refines nutritional recommendations based on individual behavior and physiological responses. Experimental evaluation shows consistent predictive accuracy, maintaining 95% performance across iterative testing from 100 to 900 posts. This framework demonstrates that machine learning can effectively translate nutritional data into personalized, real-time diet guidance, advancing the field of AI-driven health optimization.