Vibrio alginolyticus is an opportunistic marine pathogen that causes severe vibriosis in aquatic animals and occasionally in humans, leading to substantial economic losses in aquaculture. Despite progress in antimicrobial therapy, the emergence of multidrug-resistant (MDR) strains and the lack of effective vaccines have emphasized the need for novel approaches. The objective of this study was to identify and assess vaccine candidates in V. alginolyticus ATCC 17749 using an integrative strategy that integrates reverse vaccinology and immune-informatics. The complete proteome of V. alginolyticus ATCC 17749 was retrieved from NCBI databases, and computational pipelines were used to predict subcellular localization, transmembrane topology, and antigenicity. Surface-exposed, non-allergenic and non-toxic outer membrane and secretory proteins with high antigenicity scores were selected for epitope prediction. Cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes were identified through NetMHCpan 4.1, IEDB, and ABCpred, respectively. Robust humoral and cellular immune responses were predicted by immune simulation. The target proteins were docked with TLR2 and TLR4 using HDOCK, and the simulation was performed using the iMODS server for the highest-scoring docking complex for each receptor. In silico cloning to express the target protein was performed using the SnapGene tool. This integrated computational vaccinology approach reliably identifies promising antigenic targets for V. alginolyticus, providing a foundation for the rational development of next-generation polyvalent vaccines against Vibrio infections in aquaculture and related fields.