Discovering and developing medications, regulatory review, and authorisation depend critically on pharmacokinetic (PK) and pharmacokinetic/pharmacodynamic (PK/PD) models. The design of clinical trials, data interpretation, and dose ( ) computation depends critically on pharmacokinetic (PK) and pharmacokinetic/pharmacodynamic (PK/PD) models. Applying machine learning technology for PK and PK/PD prediction offers not only better results but also faster forecasts for this critical chore (Figure ). A drug-drug interaction (DDI) is the condition wherein another drug ingested or supplied concurrently modulates the way a drug is metabolised in the body (pharmacokinetics) or its effects on the body (pharmacodynamics) ( ). Drug-drug interactions (DDIs) can cause products to be removed from the market; for example, astemizole, a medication used to treat allergy symptoms, was taken off the market because it can cause the QT interval to become longer and lead to irregular heart rhythms when combined with substances that inhibit cytochrome P450 3A4 (CYP3A4), such grapefruit juice and erythromycin ( ). Taken off the market due to a sluggish heart rate and muscle breakdown when used with some cardiovascular drugs such as beta-blockers or statins, Mibefradil was used to treat excessive blood pressure and chronic chest discomfort ( ). Thus, after time-consuming and expensive drug research and development phases, it is imperative to effectively ascertain drug-drug interactions to prevent withdrawals by employing promising ML models subsequently. For example, a deep learning framework called Deepdrug has investigated drug-drug interaction in Figure ). Besides Deepdrug Figure ( ), several machine-learning methods have been created to forecast drug-drug interactions (DDIs) to address the shortage of known DDI combinations ( ). Prior research has yielded numerous models for predicting the occurrence or non-occurrence of drug-drug interactions (DDIs). These models have successfully identified DDI combinations that result in adverse effects and categorised the various forms of DDIs by utilising publicly available databases. However, the models showed limited success in identifying DDI combinations ( ).