Transmitted through mosquito bites, malaria is a disease that poses a serious threat to life and is caused by parasites. Its impact on the health of infants, children, and pregnant women globally is substantial, often leading to child malnutrition and contributing to a rise in infant mortality rates. According to the 2020 World malaria report published by WHO, there was significant increase in 14 million more cases and 69000 more deaths as compared to 2019. The emergence of insecticide and drug resistance in mosquitoes has led to setbacks in malaria control effects. Hence, early diagnosis and timely treatment are essential for effective management and control of the disease. However, traditional diagnostic methods are time-consuming, costly, and require trained personnel. Machine learning techniques can help to overcome these challenges by predicting and classifying malaria risk using clinical information. In this research paper, we propose a malaria risk prediction model using machine learning techniques based on clinical information. Our study utilizes a data set of patient records from a malaria endemic area, including demographic information, clinical symptoms, laboratory test results, and treatment history. We used machine learning classification models like logistic regression, decision tree classifiers, gaussianNB, random forests, and extratree classifiers, to build and evaluate prediction models. We used feature selection approaches to determine the most useful characteristics for malaria risk prediction. The proposed machine learning-based approach has several potential applications in clinical practice. For example, the approach can be used to identify high-risk individuals and allocate resources for early detection and treatment. The approach can also support public health officials in developing targeted intervention strategies to reduce malaria transmission. In conclusion, this study demonstrates the effectiveness of machine learning techniques in malaria risk prediction using clinical information. The proposed approach can potentially improve early detection and treatment of malaria, ultimately reducing the burden of the disease.