Malaria remains a significant global health challenge, causing hundreds of thousands of deaths annually, particularly in tropical and subtropical regions. This study proposes an advanced automated approach for malaria detection by classifying red blood cell images using machine learning and deep learning techniques. Three distinct models: Logistic Regression (LR), Support Vector Machine (SVM), and Inception-V3 were implemented and rigorously evaluated on a dataset comprising 27,558 cell images. The LR model achieved an accuracy of 65.38%, while SVM demonstrated improved classification performance with an accuracy of 84%. The deep learning-based Inception-V3 model outperformed both, achieving a classification accuracy of 94.52% after five training epochs, demonstrating its superior capability to extract intricate features from medical images. These results highlight the effectiveness of deep learning architectures in malaria diagnosis and pave the way for scalable, automated solutions, particularly in resource-limited settings.