The rapid expansion of genome sequencing projects has resulted in the identification of numerous hypothetical proteins whose functions remain uncharacterized. In Listeria monocytogenes serotype 4b, a major food-borne pathogen associated with high mortality rates, several predicted proteins lack functional annotation despite their potential role in pathogenicity and survival. In the present study, a comprehensive in silico approach was employed to functionally annotate 92 hypothetical proteins identified from the genome of L. monocytogenes. Protein sequences were retrieved and analyzed using sequence similarity searches, conserved domain identification, motif analysis, and multiple sequence alignment. Functional classification was performed based on BLAST, Pfam, and InterPro analyses. Structural prediction was performed via homology modeling via the SWISS-MODEL server, followed by structural validation and comparative analysis using PyMOL and DALI-Lite. Functional inference from structural models was supported by conserved-residue mapping and ProFunc analysis. Sequence-based analysis enabled classification of most hypothetical proteins into functional groups, including hydrolases, transferases, transporters, kinases, stress-response proteins, membrane proteins, DNA-binding proteins, and ATP-binding proteins. Structure-based modeling of selected proteins further confirmed the predicted catalytic residues, metal-binding sites, ligand-interaction sites, and conserved functional motifs. Several proteins were predicted to be involved in enzymatic activity, nucleotide metabolism, membrane transport, transcriptional regulation, and stress adaptation. This integrative computational analysis provides functional insights into previously uncharacterized proteins of L. monocytogenes. The findings enhance genome annotation quality and identify potential targets for further experimental validation, contributing to a better understanding of bacterial physiology and pathogenesis.