Chronic kidney disease (CKD) provides significant challenges to healthcare management, requiring precise prediction techniques for early detection and treatment. Traditional diagnostic addresses frequently fall short, resulting in delayed therapies and reduced patient outcomes. The study describes a unique CKD prediction system based on Machine Learning (ML) and biological information. The proposed system addresses the limitations of existing systems by utilising advanced ML algorithms capable of processing large amounts of patient data to identify subtle CKD trends. The proposed system outperforms existing systems by taking meticulous processes such as data collecting, preprocessing, feature selection, algorithm training, model optimisation, and ensemble learning. Comparative evaluations show superior accuracy, sensitivity, specificity, and AUC-ROC scores. Notably, neural networks and ensemble learning algorithms considerably improve prediction skills, with 94% accuracy, 96% sensitivity, 92% specificity, and a 97% AUC-ROC score. These results highlight the potential of the proposed system to transform CKD management by enabling early identification, lowering healthcare expenditures, and improving patient outcomes.