Computational toxicology, a field that bridges toxicology with computational tools, is transforming how adverse effects of chemicals on human health and the environment are predicted. This innovative approach reduces reliance on animal testing, accelerates safety assessments, and lowers costs for industries like pharmaceuticals and environmental regulation. The integration of data-driven models, such as machine learning algorithms and molecular simulations, is becoming critical in areas like drug discovery, environmental safety, and regulatory processes. This article explores key methodologies, including QSAR models, machine learning, and molecular docking, while highlighting real-world examples from the pharmaceutical industry and regulatory bodies. We also discuss improvements needed to overcome existing challenges in computational toxicology.