Nowadays, in modern healthcare, kidney health is of profound importance, and predictive modeling is quickly becoming an essential tool in estimating and governing renal illnesses. Therefore, this comprehensive review evaluates the current state-of-the-art prediction of kidney disease methods to show what their limitations are and where there arises a compelling need for their advancement. The existing CKD prediction systems have significant drawbacks, like inappropriate accuracy in predicting the early onset of the disease and not having a real-time monitoring capability. The constraints from the existing models expressly point to the need for an urgent innovation that will be more efficient. This paper identifies how a host of leading technologies and techniques, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), Internet of Things (IoT), wireless sensor networks (WSN), and cloud computing (CC), can be applied to both the problems and changes in prediction regarding renal disease. The first significant challenge, followed by a constraint, is the inappropriate precision of current models used for predictions. Despite all technological advancements, the current methods possibly fail to arrive at precise and timely forecasts that may cause delays in action and loss of patients. Hence, the present review aims to explore specific machine learning and deep learning techniques to reach the improved quality and reliability predicted. The second limitation is based on the failure to change or forecast in real-time from the traditional prediction systems. With the dynamic nature of kidney health over time, this is increasingly becoming a priority demand in which continuous monitoring can be done to capture subtle fluctuations in biomarkers. Given that, through this study, there is promising potential for integrating IoT and WSN into predictive models since it contributes to continuous real-time monitoring for the better proactive management of renal disease. The present review indicates the necessity of improving the prediction of renal disease beyond existing limitations. The proposed innovations are aimed at changing the outlook of renal health by employing the combined power of Quantum computing. Such an approach will look forward to improving accuracy in prediction, timely intervention, and improved patient outcomes. This confluence of technologies represents the fundamental shift in the way kidney illness is being prognosed, effectively answering the urgent need for accuracy, promptness, and continuity in renal health care.