The early diagnosis of Chronic Kidney Disease (CKD) remains a crucial challenge in medical research. This study investigates the robustness of the Modified Sequential Probability Ratio Test (MSPRT) in kidney diagnosis, focusing on its response to non-normality and outliers. Additionally, the study evaluates the diagnostic performance of MSPRT by analyzing the average sample size and the operating characteristics curve (OC) in conjunction with the Receiver Operating Characteristic (ROC) curve and the Maxwell-Boltzmann Distribution (MBD). Using patient data from the University of Maiduguri Teaching Hospital (UMTH), the study applies these statistical methods to assess their effectiveness in CKD classification. The results demonstrate the adaptability of MSPRT in non-ideal data conditions and its efficiency in minimizing sample size while maintaining high diagnostic accuracy. The findings recommend the importance of integrating statistical models such as MBD in refining diagnostic decision-making processes for CKD.