Malaria, a life-threatening disease caused by Plasmodium parasites, remains a major global health challenge, particularly in regions with limited access to medical resources. Traditional diagnostic methods, such as microscopic examination of blood smears, are labor-intensive, time-consuming, and susceptible to human error, leading to delays in diagnosis and treatment. To overcome these challenges, we propose an automated system leveraging advanced computer vision and deep learning techniques, specifically utilizing the region-based fully convolutional neural network (R-FCN) object detection model. The R-FCN model is particularly adept at identifying and localizing objects within images, making it highly suitable for the accurate detection and classification of malaria parasites. Our system is trained on a labeled dataset of approximately 1,328 images, each annotated with bounding boxes to highlight the presence of malaria parasites. Through rigorous experimentation, our proposed system has demonstrated superior performance to baseline methods, achieving higher accuracy and efficiency in parasite detection. By automating the diagnostic process, our system significantly reduces the need for human intervention, thereby minimizing errors, accelerating diagnosis, and improving patient outcomes. Moreover, this approach holds great promise for streamlining the malaria diagnosis and treatment process globally, contributing to a broader effort to combat this devastating disease and enhance public health outcomes in affected regions.