In recent years, people worldwide have been suffering from various types of kidney diseases indescribably, among which chronic kidney disease (CKD) has exacerbated the situation. Early diagnosis of CKD is the only way to hinder the advancement of kidney disease in its initial stage. However, presently doctors can use machine learning classifier algorithms to identify the disease earlier than any other existing method. Here, this research work presents a method by using eight different machine learning (ML) algorithms that can promptly detect the infection of CKD considering the health condition dataset information of the patient. A dataset is used of nearly two months of that period delivered by the hospital to identify the plausibility of chronic kidney disease. This research study has used the Extra Tree Classifier (EXT), AdaBoost (ADB), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Decision Tree (DT), Gaussian Naïve Bayes (GNB) and Random Forest (RF) to obtain an optimum result of prediction. After pre-processing the data, this research work has applied the ML algorithms and compared their performances, and eventually, the precise outcome has been obtained. The performance is analyzed by using the F1-score, precision, accuracy, recall, and AUC score. According to the analysis results, K-Nearest Neighbors and Extra Tree Classifier have performed better than other algorithms for achieving an accuracy of 99% preceding the Gradient Boost, which stands at 98%.