These limitations often stem from incomplete data, variability in patient responses, and the complexity of accurately modelling the factors influencing DDIs ( ). Also, most models have solely offered a fundamental forecast for the presence or categorization of DIs ( ). These models do not help make complex therapeutic decisions, such as accurately adjusting the dosage or selecting alternative drugs. Physicians and chemists require predictions on the fold change of PK parameters. However, thus far, no models have successfully forecasted this. Also, there is currently no available dataset that has been methodically developed to contain true-negative instances exclusively. For example, the DDI DB, such as DrugBank, is commonly utilised for predicting drug-drug interactions (DDIs). It provides information indicating the presence of a DDI between drugs A and B but does not include information indicating the absence of a DDI. Therefore, researchers have unavoidably chosen random sets of medication combinations under the assumption that there were no interactions ( ). Indeed, the lack of proof does not serve as evidence for the absence of something. Utilising a model lacking in quality input poses challenges in acquiring dependable results. When the negative set is randomly generated, it becomes challenging to pinpoint the specific reason for unanticipated problematic output ( ). As a result, there is a continuous need for more sophisticated and robust ML models to better account for these variables and improve the reliability of DDI predictions.