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Growing awareness around healthy lifestyles and the demand for automated dietary monitoring have led to the development of intelligent food-recognition systems. Traditional calorie-tracking applications depend heavily on manual entry, which often introduces inaccuracies and reduces long-term user participation. To address these limitations, this work introduces NutriTrack, a unified framework that combines YOLO-based real-time food detection, internally mapped food classes, and Nutritionix API data to produce precise nutritional estimates. In addition, the system employs machine-learning techniques to deliver personalized meal recommendations tailored to individual user needs. By supporting multi-item detection, improving calorie estimation, and integrating a comprehensive tracking interface using Flask and SQLite, NutriTrack advances beyond conventional CNN-based approaches. Evaluation results indicate strong accuracy for single-item images and reliable performance in mixed-dish scenarios, offering an efficient and user-friendly solution for everyday nutritional assessment