Overview This repository contains the de-identified dataset and supporting information for the study: "Machine Learning to Decode Medication-Related Quality of Life: A SHAP-Based Interpretability Study of Older Adults with Chronic Illness in Nepal. Study Summary As the population of older adults in Nepal grows, many individuals manage multiple chronic conditions requiring complex medication regimens. This study evaluates the predictors of medication-related quality of life (MRQoL) among 310 older adults (aged ≥ 65 years) in Nepal using a combination of machine learning (ML) algorithms and multivariate linear regression. MRQoL was assessed using the 43-item PROMPT-QoL tool, covering dimensions such as medicine information, satisfaction, and treatment impact. Usage Note This dataset is intended for researchers interested in geriatric care, pharmacy practice, and explainable AI in healthcare within resource-limited settings. For further details on variable coding, please refer to the "S1 Table" in the associated manuscript. Dataset Content The provided CSV file contains de-identified data for 310 participants, including: Target Variable: Total PROMPT-QoL scores (standardized 0–100 scale). 14 Predictor Features: Demographic and clinical data, including EQ-5D utility scores, medication adherence, number of medications, number of chronic condition, and CCI score.