Malaria is a mosquito-borne blood disease caused by Plasmodium parasites which are deadly, infectious, and life-threatening. The conventional and standard way of diagnosing malaria is by visual examination of blood smears via microscope for parasite-infected red blood cells under the microscope by qualified technicians. The given method is inefficient, time-consuming and the diagnosis depends on the experience and the knowledge of the person doing the examination. Image processing based Automatic image recognition technologies has been applied to malaria blood smears for diagnosis before. However, the practical performance has not been up to expectation. With the early prediction results, healthcare professionals can provide better decisions for patient diagnosis and treatments. This motivates us to make malaria detection and diagnosis fast, easy and efficient. To get quick results for the malaria tests, we proposed a model that involves Deep Learning and Image Processing. In this paper, we developed a model using Convolutional Neural Networks (CNNs) classifier that predicts whether the input image is malaria parasitized or not. The CNN model has many convolution blocks that detect even the tiniest possibility of plasmodium parasite present in our input. The proposed model is also evaluated using a large amount of data to increase its accuracy and correctness while detecting the malaria parasite.