Nowadays, the food business generates enormous profits, making it a prime candidate for fraud. This brings to mind the necessity of strong and dependable diagnostic methods in order to create analytical procedures for fraud detection. A new area of forensic chemistry called “food forensics” steps in to help when food is tainted or mislabeled. It is beneficial to verify food quality and safety, among other things. In India, food fraud, namely adulteration, is becoming a bigger issue. All food products, whether they are processed or raw, are frequently contaminated. Although a lot of money is spent on analyses as well as control measures, food safety is becoming more and more vital in the current world due to the various foodborne illnesses associated to infections, toxins, pesticides, adulterants, colorants, and other pollutants. It has long been believed that certain biogenic amines are the primary indicator of food quality in processed foods, serving as a watchdog over the deterioration of fresh food and food high in protein. Typical methods for detecting hazardous analytes that have been employed; however, array-based sensing strategies are becoming more and more common in order to create an exceptionally precise and accurate analytical procedure. Hence, we briefly describe and evaluate the recently published array-based sensor systems supported by machine learning and multivariate analytics here in order to maintain food quality in the field of food forensics. The fact that protein toxins, prions, and other tiny compounds that can contaminate our food can all be securely detected and quantified using the potential of multiple reaction monitoring (MRM).