Microplastic contamination in food is an escalating concern due to associated environmental and health risks, with a rising global plastic production projected to exceed 2.1 billion tons annually by 2060. This makes it essential to have effective detection and identification of microplastics for determining environmental risk and secure food safety. This study is an effort to compare conventional methods (optical detection, thermo-analytical, hyperspectral imaging) with advanced techniques (Fourier transform infrared spectroscopy, pyrolysis-gas chromatography-mass spectrometry, Raman spectroscopy) in the detection of microplastics in food. While conventional methods are effective enough in providing qualitative insights, advanced techniques provide superior sensitivity and specificity for the detection of smaller particles. The article analyses the advantages and limits of these methods, considering factors such as accuracy, cost, sensitivity, and efficiency. It also analyses the basic advantages of artificial intelligence in addressing these limitations. Artificial intelligence's speed, accuracy, and adaptability can enhance microplastic detection and identification, supporting regulatory compliance and food safety monitoring. This comprehensive analysis addresses artificial intelligence's vital role as a future research tool to the rising challenges of microplastic contamination.