This study explores applying advanced machine-learning strategies, particularly improved semantic vectors, to predict the severity of drug-drug interactions (DDIs), a crucial element in pharmacovigilance. Based on the Adverse Event Reporting System (FAERS), our study aims to analyze the combination of advanced embedding techniques with state-of-the-art machine learning (ML) algorithms to identify and quantify DDI severity. The CatBoost Classifier is the center of our analysis, as it has emerged as the most effective model in the examined trials. We improved the performance by increasing the BioWordVec Indication Substance embedding specificity, a new creation constructed through transfer learning methodologies employed on the BioWordVec model. This approach employs not only the names of the drugs but also the indications for the drugs and the active substances, forming a highly semantic network capable of capturing multiple relations between drugs. Applying BioWordVec Indication Substance embedding combined with the CatBoost Classifier, especially using the contact-vectors method, provided the best F1 score of 73. 32% and an ROC AUC score of 84%. The results imply that this method effectively models and predicts severe consequences of DDIs using deep learning that comprehensively covers pharmacological and clinical aspects. Based on our results, we suggest incorporating semantic embedding and ML into the pharmacovigilance processes to improve the predictive potential of DDI evaluations. Thus, by enhancing the body of knowledge related to the analytical methods of assessing drug interactions, the present study substantially enhances the quality of clinical decision-making and patient protection. The novel embedding marks a significant step forward in the methodology, providing a more solid tool for the fine-grained dissection of the complexities needed in modern medicine, where multiple drug therapies are now the norm.