The development of sophisticated frameworks for the accurate and efficient prediction of drug toxicity is becoming more essential due to the growing complexity of pharmacological research. We provide an automata-based adaptive framework for improving predictive models of drug toxicity, concentrating on the Indian pharmaceutical sector. This methodology improves predictive accuracy by amalgamating automata theory with machine learning approaches, including deep learning models, to adapt and develop in reaction to novel inputs. In India, several genetic and environmental variables contribute to a markedly increased incidence of adverse drug reactions (ADRs). The framework employs many datasets, including clinical trials, molecular attributes, and epidemiological data. The system's versatility enables it to use real-time data from healthcare databases and pharmaceutical reports to augment and modify its prediction models rapidly. The suggested method seeks to enhance patient safety, optimize pharmaceutical development, and diminish the occurrence of drug-induced toxicity by focusing on India's unique demographic and genetic diversity. The technique facilitates more efficient and focused pharmaceutical safety evaluations in India by providing dynamic and context-specific suggestions, hence acting as a significant resource for researchers and healthcare providers.