Artificial Intelligence (AI) has revolutionized modern medicine by providing computational solutions to manage complex clinical data and improve therapeutic outcomes. In pharmacology, AI particularly machine learning (ML) and deep learning (DL) models has demonstrated significant potential in predicting drug–drug interactions (DDIs), a major cause of adverse drug reactions (ADRs) and increased healthcare costs. This study focuses on the DANN-DDI (Deep Attention Neural Network for Drug–Drug Interaction) model, which integrates diverse pharmacological data to enhance the accuracy of DDI prediction. Drug features including chemical substructures, targets, enzymes, pathways, and existing interactions were extracted from the DrugBank (version 5.1.0) and KEGG databases. The DANN-DDI framework consists of three components: drug feature learning, drug-pair feature learning, and interaction prediction using a deep neural network optimized via the Adam algorithm and binary crossentropy loss. Model performance was evaluated using 5-fold cross-validation and assessed through AUC, AUPR, accuracy, and F-measure metrics. The results indicated that optimal parameters (embedding dimension = 128, 7 hidden layers, 150 epochs, dropout rate = 0.4) yielded superior prediction outcomes. Compared with traditional computational methods such as similarity analysis and matrix factorization, the DANN-DDI model demonstrated improved capability to detect potential DDIs effectively. Overall, this study highlights the value of integrating AI-based approaches into pharmacovigilance systems to predict and prevent harmful drug interactions, ultimately enhancing patient safety and treatment efficacy.