Fungal infections pose a significant global health challenge, necessitating the continuous development of novel antifungal agents. Computational approaches have emerged as powerful tools in antifungal drug discovery, enabling efficient molecular design, screening, and optimization. This review explores advancements in computational strategies, including molecular docking, pharmacophore modeling, and artificial intelligence-driven drug design, to enhance antifungal therapy. A key focus is on the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling of antifungal compounds, ensuring improved bioavailability and reduced toxicity. Additionally, we discuss the integration of cheminformatics and molecular dynamics simulations to predict antifungal potential and resistance mechanisms. The synergy of computational techniques with experimental validation accelerates the identification of promising antifungal candidates, offering a cost-effective and time-efficient alternative to traditional drug discovery methods. Future perspectives highlight the need for multi-targeted drug design, predictive machine learning models, and enhanced databases for antifungal research. The continuous refinement of computational methodologies is essential to overcoming current limitations in antifungal therapy and addressing the rising threat of drug resistance.