International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com SATHYA LAHARI 7 4 (July - August) 2025 08 July-August enHospital Stay LengthMachine LearningLogistic RegressionMLPRandom ForestXGBoostSHAP. Predicting the length of hospital stay LOS is critical for improving healthcare resource management and patient care This study investigates the application of explainable machine learning techniques to forecast hospital stay duration using a dataset from Kaggle comprising various patient and hospitalrelated features The primary goal is to develop accurate predictive models and elucidate the underlying factors influencing hospital stay lengths The study employs multiple machine learning algorithms including Logistic Regression MultiLayer Perceptron MLP Random Forest Gradient Boosting and XGBoost Each models performance is evaluated using standard metrics such as accuracy precision recall and F1score Additionally explainability tools such as SHapley Additive exPlanations SHAP are utilized to interpret model predictions and identify the most significant predictors of LOS The findings demonstrate that advanced machine learning models particularly ensemble methods achieve superior predictive accuracy Moreover the explainability analysis provides valuable insights into the critical factors influencing hospital stays thereby enabling healthcare practitioners to make informed decisions and optimize hospital resource allocation This research underscores the potential of integrating explainable machine learning into healthcare analytics to enhance operational efficiency and patient outcomes Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2025 All Rights Reserved