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In recent years, food safety has emerged as a critical public health issue due to the increasing occurrences of foodborne illnesses. These illnesses are often caused by toxic substances, pathogens, and chemicals present in contaminated food products. Conventional methods for determining the presence of bacteria and poisons that are transmitted through food can be laborious, time-consuming, and need sophisticated laboratory equipment. To address these challenges, we propose the development of a portable and cost-effective food poisonous detection device designed for rapid and accurate identification of harmful contaminants in various food items. The proposed device leverages advanced sensor technology, microfluidics, and artificial intelligence (AI) to detect a wide range of foodborne pathogens, toxins, and chemical residues. The device is equipped with biosensors that can specifically bind to target molecules such as bacteria, viruses, and toxins, generating an electrical signal that is proportional to the amount of the pollutant present. An integrated artificial intelligence algorithm performs additional processing and evaluation on the signal, which compares the data against a pre-established database of known contaminants to provide an accurate and timely diagnosis. The development process involved extensive research and testing to ensure the device's sensitivity, specificity, and accuracy. Various types of biosensors, including optical, electrochemical, and piezoelectric sensors, were evaluated for their ability to detect different contaminants. The integration of microfluidic channels enabled efficient sample handling and reduced the overall testing time. The AI algorithm was trained using a large dataset of foodborne pathogen profiles to enhance its predictive capabilities and reduce false positives and negatives.