Mr. N.Chandira Prakash, Dyagari Sathvika, Rachala Charitha, Kallem Supriya, Peram Bharghav Reddy 02 11 Detecting sideeffects arising from adverse drugdrug interactions DDIs has become a crucial focus in modern pharmacovigilance driven by the widespread use of polypharmacy and the growing need for automated datadriven safety monitoring Existing research demonstrates significant progress in DDI prediction adverse drug reaction ADR detection and pharmacological feature modeling through methods such as label propagation multidimensional feature fusion graph neural networks GNNs and deep neural architectures 114 Recent studies emphasize the increasing reliance on realworld evidence spontaneous reporting systems electronic health records and curated datasets such as TWOSIDES OFFSIDES DrugBank and FAERS to improve the reliability of interactionbased ADR identification 47 1520 Building upon these advancements this work proposes an enhanced DDIbased sideeffect detection framework that integrates molecular representation learning spatiostructural drugfeature fusion and signaldetection analysis to accurately identify harmful interactioninduced reactions Leveraging insights from networkbased inference statistical disproportionality methods and interpretable machine learning models the system aims to improve prediction accuracy while reducing false positive signals The study contributes a unified analysis of traditional pharmacovigilance techniques and contemporary AIdriven approaches highlighting their strengths limitations and applicability to realworld clinical settings The proposed model aligns with emerging trends in intelligent drugsafety surveillance and offers a scalable explainable solution suitable for largescale healthcare environments Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026