Food allergies and nutrient sensitivities pose significant health challenges, impacting individuals' quality of life and increasing healthcare costs. Predictive modeling using advanced machine learning techniques offers a promising approach to enhance early detection, personalized management, and preventive strategies for these conditions. This research explores the application of sophisticated machine learning algorithms, including ensemble methods, deep learning, and feature selection techniques, to predict food allergies and nutrient sensitivities based on dietary logs, genetic information, and clinical data. We employed a diverse set of machine learning models such as Random Forests, Gradient Boosting Machines, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to analyze and interpret complex data patterns. The study integrated data sources including patient medical histories, genetic predispositions, and dietary intake logs to develop robust predictive models. Model performance was evaluated using metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC). The findings indicate that advanced machine learning techniques can significantly improve the prediction accuracy of food allergies and nutrient sensitivities. These models provide insights into the underlying patterns and correlations between dietary habits, genetic factors, and allergy manifestations. By enabling early detection and personalized dietary recommendations, these predictive models hold the potential to enhance individual health management and contribute to more effective public health strategies. Future research should focus on refining these models, expanding data sources, and validating the predictions in diverse populations to ensure generalizability and practical applicability.