The future of intelligent biosystems is bright, with major strides projected as a result of new discoveries and innovations. A number of leading multinational companies have integrated intelligent biosystems to uplift food quality. For example, Nestlé uses biosensing in production to track microbial contamination. IBM is adopting blockchain to ensure transparent food traceability, while BluWrap monitors the oxygen and temperature of fresh fish pallets with smart packaging to maximize shelf life and reduce carbon footprint. At the same time, biosensors supported by the Internet of Things (IoT) can help farmers, stakeholders, and the agri-food industry through rapid testing and predictive analytics based on sensor-generated computing using Artificial Intelligence (AI). The integration of AI methodologies, including cluster analysis and classification algorithms, with biosensors can bridge the gap between data collection and analysis and advance the accuracy of data handling throughout the food supply chain. The methodology of this work follows a systematic review of the literature on intelligent biosystems and AI tools in food safety, evaluating biosensing techniques, challenges, and scalability, and exploring future directions. The potential of these tools is conspicuous, although their application in real-world scenarios is still limited due to lack of focus, implementation costs, scalability, and well-adapted and regulatory framework research. In this review, we search for results that examine the state of the art of AI for food quality control, highlighting the impact of smart biosensors that offer advanced real-time monitoring, predictive analytics, optimization, enhanced traceability, and consumer empowerment to improve risk management and ensure high standards of food processing and safety, as well as public health and economic integrity.