As the demand for fresh seafood through online platforms like Tender Cuts and Licious grows, consumer concerns about product freshness and safety persist. Many consumers lack visibility into critical factors such as packaging dates, the use of preservatives, and the shelf life of the seafood they purchase. This research presents an AI-driven solution integrating advanced optical and electrochemical sensors to assess fish quality. Key freshness indicators, including gill color, eye clarity, and scale condition, are analyzed to provide a reliable evaluation of fish freshness. Additionally, electrochemical sensors detect the presence of harmful chemicals, ensuring the seafood is safe for consumption. Machine learning algorithms, specifically Naive Bayes and Random Forest, are utilized to analyze the sensor data, combining probabilistic classification and ensemble learning to achieve high accuracy in fish quality assessment. This innovative approach offers consumers a data-driven, trustworthy solution for purchasing fresh, safe, high-quality seafood online.