 Malaria is a blood disease caused by the plasmodium parasites transmitted through the bite of female anopheles mosquitoes. Microscopists typically examine thick and thin blood smears to diagnose the disease and calculate parasite mia. However, accuracy can depend on smear quality and expertise in identifying and counting infected and uninfected cells. This process can be time-consuming and difficult when dealing with large numbers of samples. State-of-the-art image analysis techniques using machine learning, ML, and convolutional neural networks, CNNs, have been developed to automate the process of diagnosing malaria. These techniques are able to extract features from microscopic images of the smears and classify them into either infected or uninfected cells. The results show that these techniques are more accurate than manual microscopy and can provide faster and more reliable diagnoses.