The integration of Artificial Intelligence (AI) into drug discovery is revolutionizing therapeutic development by leveraging biochemical insights. This study employs a cross-sectional design to evaluate AI's efficacy in identifying novel drug targets and optimizing treatments for melanoma and resistant psoriasis. AI models, such as convolutional neural networks (CNNs), analyzed genomic, proteomic, and clinical datasets to uncover actionable targets, predict therapeutic responses, and accelerate drug development timelines. Key biochemical parameters were assessed, revealing significant alterations in serum biomarkers, gene expression profiles, and enzyme activities associated with AI-driven therapies. For instance, elevated cytokine levels and upregulation of anti-apoptotic genes were noted in melanoma patients, while psoriasis treatment showed increased expression of inflammatory markers and altered lipid metabolism pathways. Clinical validation through case-control studies demonstrated that AI-derived therapies significantly improved disease severity indices, quality-of-life scores, and progression-free survival rates, while maintaining a comparable safety profile to standard care. These findings highlight AI's transformative potential in advancing drug discovery by enhancing precision, efficiency, and therapeutic impact, particularly for complex skin diseases.