The rapid growth of digital food platforms has reshaped how individuals discover and prepare meals, yet most existing recipe systems remain confined to text-based keyword search and lack meaningful visual understanding or adaptive personalization. This paper presents Snap Cook, an AI-driven cooking assistance platform that identifies food items directly from images and delivers personalized recipe recommendations through an integrated pipeline combining computer vision, hybrid machine learning recommendation logic, and rule-based dietary filtering.The system employs a fine-tuned Convolutional Neural Network (CNN) trained on over 12,000 labeled images from the Food-101 dataset augmented with custom samples, achieving an image recognition accuracy of 94.2%. A content-driven recommendation engine built with Scikit-learn matches recognized dishes to a curated recipe catalog, while a rule-based dietary filtering layer handles vegetarian, vegan, gluten-free, and allergen-based constraints with 97.1% filtering accuracy. The full-stack implementation uses Flask (backend), React.js (frontend), PostgreSQL (database), and OpenCV (preprocessing), containerized via Docker for reproducible deployment.Experimental evaluation across five functional modules yields a system-level mean accuracy of 92.1% and an F1-score of 92.1%, outperforming baseline text-search platforms on visual discovery and personalization benchmarks. Limitations including dataset scope, real-time nutritional API integration, and offline accessibility are acknowledged, and a roadmap for future enhancement through generative AI, augmented reality guidance, and multilingual support is presented.