Drug-drug interactions (DDIs) threaten patient safety and treatment results, especially with multiple drugs. Traditional methods like clinical trials and rule-based systems have limits in speed and scale. To improve detection, many now use artificial intelligence (AI) and machine learning (ML). These methods rely on a variety of data sources, including drug databases, patient records, and medical literature. They face challenges such as data quality, standardization, and missing information. Different AI models are used, like similarity-based systems, network models, graph neural networks, and deep learning approaches. Natural language processing (NLP) helps gather information from unstructured texts like clinical notes. AI tools are already used in hospitals and safety monitoring, guiding doctors and spotting rare interactions. Still, issues like model interpretation, data gaps, and fitting into clinical workflows remain. Future efforts focus on making AI more understandable, handling multiple data types, protecting privacy, and customizing medicine. Overall, AI is changing how DDIs are found and managed, helping improve drug safety and personalized treatment.