Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools with immense potential to transform various aspects of clinical research and pharmacovigilance. This paper explores the role of AI and ML in these fields, highlighting their applications, benefits, and challenges. In clinical research, AI and ML offer significant advancements in data analysis, patient stratification, and decision support. These technologies can efficiently process large volumes of clinical data, including electronic health records, genomic data, medical imaging, and clinical trial data, to identify patterns, predict outcomes, and generate actionable insights. One major application of AI and ML in clinical research is patient stratification and personalized medicine. These technologies can analyze patient data to identify subgroups with specific characteristics or treatment responses. By understanding these subgroups, researchers can design more targeted clinical trials, identify appropriate patient populations for specific interventions, and develop personalized treatment approaches. Additionally, AI and ML can aid in the identification of potential therapeutic targets, prediction of treatment responses, and optimization of clinical trial designs. These technologies can analyze complex datasets, uncover hidden correlations, and generate hypotheses that can guide researchers in their investigations. Furthermore, AI and ML can assist in adverse event prediction and monitoring, aiding in early detection and intervention. In the field of pharmacovigilance, AI and ML have the potential to revolutionize adverse event detection, signal generation, and signal validation. These technologies can process large volumes of real-world data, including electronic health records, social media, and spontaneous reporting systems, to identify potential safety concerns associated with medications. AI and ML algorithms can detect patterns, associations, and unexpected relationships between drugs and adverse events, enabling proactive pharmacovigilance activities. The integration of AI and ML in pharmacovigilance also facilitates signal validation and risk assessment. These technologies can analyze diverse data sources, identify potential signals, and prioritize them based on their significance and impact. By automating certain aspects of signal validation, AI and ML can streamline the pharmacovigilance process, allowing for more efficient and timely identification and management of drug safety concerns.