Botulinum toxin A (BoNT-A) is widely used in both therapeutic and aesthetic settings; however, the formation of neutralizing antibodies (NAbs) remains a critical concern, leading to treatment failure. Immunogenic responses are known to vary between individuals due to HLA polymorphisms. Although some claim that neurotoxin-associated proteins (NAPs) shield BoNT-A from immune detection or are themselves immunogenic, there is limited molecular evidence supporting either view. This study applies computational immunogenetics to explore BoNT-A immunogenicity, focusing on HLA binding and the influence of accessory proteins. Epitope mapping, molecular docking, and HLA binding predictions were used to evaluate interactions between BoNT-A epitopes and selected class II HLA alleles (HLA-DQA1*01:02, HLA-DQA1*03:03, HLA-DQB1*06:04, HLA-DQB1*03:01, and HLA-DRB1*15:01). To assess the potential immunomodulatory role of NAPs, molecular dynamics (MD) simulations, solvent-accessible surface area (SASA) analysis, and electrostatic potential mapping were also conducted. Key epitopes—L11, N25, and C10—showed strong binding affinities to HLA-DQA1*01:02, HLA-DQB1*06:04, and HLA-DQA1*03:03, indicating a potential immunodominant role. NAPs did not obstruct these epitopes but slightly increased their exposure and appeared to stabilize the toxin structure. Electrostatic mapping and binding free energy calculations suggested no significant immunogenic shift in the presence of NAPs. BoNT-A immunogenicity appears to be influenced by HLA allele variability, reinforcing the value of patient-specific genetic profiling. The presumed immunogenic role of NAPs remains unsubstantiated at the molecular level, underscoring the need for evidence-based evaluation over commercial rhetoric. While these findings provide valuable molecular insight, it is important to acknowledge that they are derived entirely from in silico analyses. As such, experimental validation remains essential to confirm the immunological relevance of these predicted interactions. Nonetheless, this computational framework offers a rational basis for guiding future clinical research and the development of HLA-informed BoNT-A therapies.