Abstract

The convergence of big data, machine learning (ML), and artificial intelligence (AI) has catalyzed a paradigm shift across biomedical and ecological sciences. This review explores the transformative role of predictive analytics in three interlinked domains: drug discovery, disease monitoring, and mycology. In drug development, predictive tools have accelerated the identification of promising compounds, optimized lead selection, and improved toxicity forecasting dramatically reducing cost and time. Deep learning architecture and graph-based models are now routinely used to design novel therapeutics and screen compound libraries with high precision. Mycology, though historically underrepresented in computational biology, is gaining from predictive analytics through automated fungal classification, ecological trait modeling, and biosurveillance applications. Advances in image-based recognition and genomic trait prediction are fostering new avenues for fungal biodiversity research and natural product discovery. Despite these achievements, challenges persist ranging from data heterogeneity and model interpretability to regulatory constraints and ethical considerations. This review outlines current limitations and proposes a roadmap for integrating multimodal datasets, enhancing model transparency, and expanding access to predictive tools across domains. By uniting developments in drug discovery, public health, and fungal research, this review highlights the growing synergy between predictive analytics and life sciences. The integration of these tools into real-world systems offers a pathway to faster therapeutics, smarter diagnostics, and improved ecosystem management.