This project presents a computational bioprospecting pipeline aimed at identifying potent antimicrobial peptides (AMPs) from microbial whole-genome sequences (WGS) for antibacterial therapeutic applications. Three microorganisms were investigated — Bacillus cereus, Lactobacillus rhamnosus, and Streptomyces albidoflavus — with genome data sourced from the NCBI SRA database. The analytical workflow integrated multiple bioinformatics tools: BV-BRC for genome assembly, antiSMASH for biosynthetic gene cluster identification, AntiBP for AMP probability scoring, CAMPR3 for machine learning-based antimicrobial activity prediction, and APD3 for physicochemical property analysis. Peptides were ranked based on key properties including net charge, hydrophobic ratio, Boman Index, Wimley hydrophobicity, and molecular weight Among the three organisms, Bacillus cereus (SRA: SRR31059636), isolated from the human gut at Johns Hopkins University, yielded peptides with the highest predicted antibacterial potency. The top candidate, PID 314, demonstrated the most favorable combination of positive net charge (+5.0), Boman Index (5.94 kcal/mol), and Wimley hydrophobicity (11.12), suggesting strong membrane-targeting potential. This work contributes to the growing effort to address antimicrobial resistance (AMR) by identifying novel peptide candidates through in silico screening.