Proteases from Bacillus species are of significant industrial and biotechnological interest due to their diverse applications. This study focused on the in silico characterization of Bacillus protease enzymes to elucidate their physicochemical properties and to establish a classification framework based on computational analyses. Initially, a comprehensive dataset of 100 Bacillus protease sequences, categorized into 6 groups—(1) metalloprotease, (2) serine protease, (3) cysteine protease, (4) glutamic protease, (5) aspartic protease, and (6) threonine protease—were retrieved from the GenBank database and were then subjected to analysis using various bioinformatics tools (e.g., ProtParam, Pfam, and MEME) to characterize their key features, including their number of amino acid residues, amino acid composition, number of positive/negative charges, molecular weight, theoretical isoelectric point (pI), extinction coefficient, instability index, aliphatic index, and grand average of hydropathicity (GRAVY). Moreover, phylogenetic analysis and comparative sequence analysis were employed to investigate evolutionary relationships and identify conserved motifs, facilitating the categorization of these proteases into distinct groups. The in silico approach enabled the rapid prediction of diverse physicochemical characteristics, revealing significant variations among different Bacillus proteases. The resulting classification provided a systematic understanding of their potential functionalities and optimal operational conditions. These findings offer valuable insights for selecting or engineering Bacillus proteases with desired traits for specific industrial or research applications, streamlining the traditional experimental characterization process. In particular, understanding these enzymes' properties can aid in identifying candidates suitable for food microbiology applications, such as fermentation, protein hydrolysis, and food preservation, where tailored protease activity is critical.