Biofilm formation on food contact surfaces poses significant challenges to food safety due to increased resistance to disinfectants and risk of cross-contamination. This study aims to develop a machine learning–enhanced nanosensor array for the classification and prediction of bacterial biofilms on food processing surfaces, particularly stainless steel. A fluorescence-based sensor array composed of 2D nanoparticles and ssDNA probes was designed to capture compositional and developmental features of biofilms formed by four pre-screened species: Escherichia coli, Staphylococcus aureus, Salmonella enterica, and Pseudomonas fluorescens. Biofilms were cultivated in 96-well plates and on stainless steel coupons across three timepoints (days 1, 3, and 5). Sensor responses were recorded and analyzed using three machine learning algorithms—random forest (RFC), support vector classification (SVC), and multilayer perceptron (MLP)—to classify species and biofilm maturity (12 total classes). Data preprocessing included normalization, imputation, and stratified train-test splitting. Model performance was evaluated using accuracy, F1 score, and ROC-AUC with a one-vs-rest strategy. Preliminary results indicate species-specific fluorescence patterns and distinct clustering in PCA analysis. The sensor array is expected to achieve over 90% classification accuracy, with slightly lower performance for early-stage biofilms. This approach enables rapid, label-free biofilm identification and maturity assessment, offering a scalable solution for real-time monitoring of microbial contamination in food environments. The findings support the integration of advanced sensing and machine learning techniques for biofilm control strategies in food safety applications.