Chronic Kidney Disease (CKD) poses a significant global health burden, necessitating advanced diagnostic tools for early detection and intervention. This research delves into the complexities of utilizing machine learning techniques for CKD diagnosis and presents a comprehensive framework to address critical challenges. Firstly, the study investigates contemporary issues related to Data Quality and Quantity, emphasizing the need for diverse, high-quality datasets to enhance the accuracy and reliability of machine learning models. Secondly, it explores challenges associated with Feature Selection and Interpretability, highlighting the importance of identifying relevant features within vast medical datasets and ensuring the transparency of decision-making processes.