This research explores the convergence of machine learning, specifically employing the Random Forest Classifier algorithm, with Internet of Things (IoT) integration to predict kidney diseases. The study, conducted on a comprehensive dataset, yields promising results with an impressive accuracy of 98%. The utilization of the Random Forest Classifier demonstrates robust predictive capabilities, as evident in the confusion matrix, which reveals minimal instances of misclassification with only one false positive and one false negative. The classification report further emphasizes the model's precision and recall, showcasing high F1-scores of 99% for non-kidney disease instances and 98% for kidney disease instances. This research highlights the potential of the Random Forest Classifier algorithm in healthcare decision-making, especially when integrated with IoT technologies, offering a reliable and sustainable approach to kidney disease management.