Artificial intelligence (AI) and machine learning (ML) are fundamentally transforming methodologies in the life sciences and environmental surveillance. These technologies enable the analysis of complex biological systems and the monitoring of ecological changes with unprecedented precision and scale. This review examines the deployment of AI and ML in key areas, including genomic sequence interpretation and protein structure prediction in bioscience, as well as real-time air quality assessment and wildlife population monitoring in environmental science. Sophisticated computational architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and ensemble methods, are employed to process vast, heterogeneous, and complex datasets. Comparative analyses demonstrate significant outcomes, including over 95% accuracy in protein structure prediction and enhanced precision in pollution modelling. However, persistent challenges include issues of data equity, inherent algorithmic biases, and substantial computational resource requirements. In conclusion, AI and ML are driving more robust scientific discovery in biology and enabling more intelligent planetary stewardship, fostering interdisciplinary collaboration to connect cellular-level mechanisms with global ecosystem health.

Keywords: Artificial Intelligence; Machine Learning; Bioscience Research; Environmental Monitoring; Air Quality Forecasting;