Readmission in 30 days continue to be a prime focus for health providers, showing potential inefficiency in patient treatment and rising cost. This research discusses the potential application of machine learning algorithms in determining which patients have a higher chance of being readmitted and could benefit from earlier intervention as well as more effective discharge planning. With an actual healthcare database, important features of patients that were examined include age, days in hospital, discharge diagnosis, and prior admission status. Following careful data pre-processing and feature extraction, the four machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB) were implemented for training and testing the models. SVM demonstrated the best predictive accuracy, especially in terms of recall and accuracy, and logistic regression gave explainable results. The K-Nearest Neighbor less accurate but easier to implement. The models were evaluated using standard evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC score. The research proves that predictive analytics can efficiently assist hospitals in the identification of risk patients, maximizing care, and minimizing avoidable readmission. The results reveal increasing importance for data science in improving healthcare provision and paving the pathway for future integration of machine learning with clinical decision-support systems.