The increasing resistance of Plasmodium spp. and Trypanosoma brucei to conventional antimalarial and trypanocidal drugs poses a major challenge for global health. In response, this study introduces a computationally-driven hybrid multi-target drug design strategy, using AutoEvoChem V2.0, a platform developed by the author. I propose a series of unprecedented candidate molecules derived from drug repurposing and rational hybrid design, including derivatives of Rufinamide, Disulfiram, Clofazimine, Sulforaphane, and Benserazide. These hybrids combine molecular fusion, controlled pro-oxidant motifs, peptide targeting, and nanoparticle encapsulation, aiming to enhance efficacy and selectivity against malaria and human African trypanosomiasis (HAT). Computational docking and ADMET predictions indicate that these hybrids possess strong multi-target activity, synergistic mechanisms, and favorable pharmacokinetic properties, potentially overcoming limitations of current monotherapies. This study establishes a proof-of-concept framework for designing next-generation multi-mode anti-parasitic therapies and highlights the potential of computational innovation in accelerating drug discovery for neglected diseases.