Antimicrobial resistance represents one of the most critical global health challenges of the 21st century, urgently demanding innovative strategies for antimicrobial discovery. Traditional antibiotic development pipelines are slow, costly, and increasingly ineffective against multidrug-resistant pathogens. In this context, recent advances in artificial intelligence have emerged as transformative tools capable of accelerating antimicrobial discovery and expanding accessible chemical and biological space. This comprehensive review critically synthesizes recent progress in AI-driven approaches applied to the discovery and design of both small-molecule antibiotics and antimicrobial peptides. We examine how machine learning, deep learning, and generative models are being leveraged for virtual screening, activity prediction, mechanism-informed prioritization, and de novo antimicrobial design. Particular emphasis is placed on graph-based neural networks, attention-based and transformer architectures, and generative frameworks such as variational autoencoders and large language model-based generators. Across these approaches, AI has enabled the identification of structurally novel compounds, facilitated narrow-spectrum antimicrobial strategies, and improved interpretability in peptide prediction. However, significant challenges remain, including data scarcity and imbalance, limited experimental validation, and barriers to clinical translation. By integrating methodological advances with a critical analysis of the current limitations, this review highlights emerging trends and outlines future directions aimed at bridging the gap between in silico discovery and real-world therapeutic development.

Antimicrobial resistance (AMR) has severely compromised the clinical utility of classical sulfonamide antibiotics, primarily owing to the emergence of resistance‐associated dihydropteroate synthase variants. In this study, sixteen N‐(1H‐indazolyl) aryl sulfonamide hybrids were evaluated as potential DHPS inhibitors against Escherichia coli and Staphylococcus aureus using disk diffusion, broth microdilution (MIC ₈₀ ), and minimum bactericidal concentration (MBC) assays. Active compounds were characterized by in silico ADME and toxicity profiling, with molecular docking performed against wild‐type DHPS and the sulfonamide‐resistant Sul1 variant. Antibacterial activity was species‐dependent: compound 4 showed the strongest activity against E. coli (MIC = 32 µg/mL), while compounds 11 and 16 were most potent against S. aureus (MIC = 32 µg/mL), outperforming sulfathiazole and sulfisoxazole. All hybrids showed superior docking affinities to sulfamethoxazole (SMX), with compound 11 showing the highest affinity for wild‐type DHPS (−7.79 kcal/mol); compound 4 retained favorable binding against Sul1 (−6.83 kcal/mol), suggesting potential resistance resilience. Structure–activity analysis showed antibacterial efficacy is governed by aryl sulfonyl electronic effects, steric balance at the C7 indazole position, and physicochemical properties affecting Gram‐negative membrane permeability. These findings support indazole–sulfonamide hybrids as promising scaffolds that may retain predicted binding complementarity toward the Sul1‐resistant DHPS variant, warranting validation against resistant isolates and purified DHPS enzymes.