This research presents Drug Interaction AI, a mobile application powered by advanced artificial intelligence (AI) algorithms (Paul et al. in Drug Discov Today, 2021 [1]), designed to enhance medication safety and prevent adverse drug interactions. The system utilizes deep learning models and neural networks to analyze complex drug combinations, identify potential risks, contraindications, and adverse effects, and provide real-time, actionable insights. To ensure robust and scalable predictions, the application leverages a comprehensive medical database compiled from electronic health records (EHRs), pharmaceutical research, and FDA drug interaction reports. This enables real-time analysis and supports informed decision-making for both patients and healthcare providers (Fatima et al. in J Oral Maxillofac Surg 82, 2024 [2]). The proposed application integrates a strong architectural framework that combines machine learning models with neural network techniques, ensuring accurate and scalable drug interaction detection. Features such as real-time alerts, drug-specific usage guidelines, and personalized medication safety insights enhance its usability. The system is further optimized with secure data pipelines, a modular API-driven design, and user-friendly mobile and web interfaces to ensure accessibility and reliability. This work contributes to the advancement of AI-driven precision medicine by enabling data-driven clinical decision-making. Further research is recommended to enhance multilingual support, integrate predictive analytics for drug reaction severity, and expand the database to include emerging pharmaceuticals, ensuring global applicability and impact.