<p>Artificial intelligence (AI), Machine Learning (ML), and Deep Learning (DL) revolutionize biological science by creating innovative approaches to examine intricate and complex datasets that would be unattainable and beyond human capability, but the effective integration of these technologies remains challenged because of their complexity, heterogeneity, and diverse scale of biological datasets. The traditional approaches struggle to interpret accurate results generated from genomics, proteomics, and microbiome studies. So, to address this kind of limitation, this review article addresses the most recent computational methodologies integrated with AI. AlphaFold, an AI-driven algorithm played a crucial role in predicting protein structures and resolving the protein folding problem and enabling the analysis of protein–protein interactions, thereby accelerating drug discovery and biomarker identification. Nowadays AI has become crucial as it helps to tackle the challenges of sustainable development and global health initiatives by focusing mostly on energy security, climate change, and environmental conservation. Also, AI assisted algorithms help to protect biodiversity by analysing ecosystems and microbial interactions. From choosing personal diet food to diagnosing a genetic disease, AI is transforming scientific discovery in every discipline. This review adopts a comprehensive perspective that connects molecular biology, microbiome research, public health, and sustainable development, while critically examining recent methodological advances that amplify accuracy and diminish error with challenges related to data quality, model interpretability, and ethical considerations. The findings indicate that AI-enabled bioinformatics approaches substantially improve pathogen detection, disease outbreak prediction, and ecosystem modelling, thereby supporting precision medicine, sustainable agriculture, biodiversity conservation, and global health initiatives.</p>