Malaria is a fatal disease. It is transmitted from one person to another by the bite of female Anopheles Mosquitoes. The estimated cost of detection of malaria in India is 11,640 crore per year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard but it takes more time. The proposed method not only improved the degree of accuracy of the analysis but it provides double checked to verify the correct results. The proposed work used various image processing techniques like image acquisition, image pre-processing, segmentation, feature extraction using regional descriptor, feature detection using edge detector and then classification using particle swarm optimization support vector machine. The classification work in two levels, the first level is applicable for checking the presence of malaria parasite in red blood corpuscles and the second level is detect the type of malaria parasite using particle swarm optimization support vector machine. This proposed work is mainly focus on detection accuracy, false match rate, computational time, less estimation time for parasite detection and complexity. In this way, the detection of malaria parasite may be give faster and accurate results. Keywords: Malaria parasite, image processing, Image pre-processing segmentation, Feature extraction, classification.