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In today's world, everyone tries to be health-conscious, but owing to work and a hectic schedule, people only pay attention to their health when signs of sickness appear. Chronic Kidney Condition is a disease that does not have any symptoms or, in some situations, does not have any disease-specific signs. As a result, it is difficult to forecast, identify, and prevent such a sickness manually, which could result in lasting health damage. Machine learning, which excels at prediction and analysis, provides a ray of hope in this dilemma. We studied CKD patient data and presented a system for predicting CKD risk using machine learning algorithms such as Logistic Regression, Random Forest, and K-Nearest Neighbor (K-NN). We used data from 455 patients. Here, an online data set from the UCI Machine Learning Repository and a real-time dataset from Khulna City Medical College are employed. For the development of our system, we used Python as a high-level interpreted programming language. We used a 10-fold CV to train the data using a Hybrid ensemble technique. The hybrid ensemble technique achieves 97.12 % accuracy, whereas ANN achieves 94.5 %. This technology will aid in the early detection of chronic kidney disorders..