A Decision Tree-based machine learning application is designed to provide personalized dietary advice tailored to users’ health conditions and climatic variations. The Health Module employs classification algorithms to recommend specific foods that aid in managing a range of medical issues. By analyzing user health parameters such as disease type and severity, the system ensures recommendations align with individual nutritional needs. The Climatic Conditions Module integrates real-time weather data, including climate type, temperature, and season, to suggest optimal meals suited to current environmental conditions, encouraging adaptive and sustainable eating habits. Advanced data visualization tools, such as bar and pie charts, are incorporated to offer users intuitive insights into their dietary patterns and the nutritional impact of their choices. By combining tailored health recommendations with climate-aware meal suggestions, the application aims to promote well-being and empower users to make informed dietary decisions, fostering a balanced approach to nutrition.