Malaria is a disease caused by parasites of the genus Plasmodium, which can be transmitted through the bite of a mosquito or even through transfusions of infected blood. This represents one of the main health problems for developing countries. Despite the existence of conventional procedures for the eradication and control of this disease, there are still some challenges in geographically remote areas, where the lack of accessibility, equipment and specialized training can lead to inadequate treatments. From this perspective, early detection of this illness is crucial. Faced with these challenges, recent artificial intelligence advances offer new opportunities to optimize the identification of diseases and pathologies. Along these lines, the aim of this paper is to develop a tool based on convolutional neural network to support the detection of malaria using blood samples. In this context, a set of images was identified from the Kaggle platform. This set identifies two types of conditions for blood samples: malaria infection, non-malarial infection. Then, through CRISP-DM (Cross Industry Standard Process for Data Mining), this research evaluated four convolutional neural networks and chose the most optimal one to implement within a web application. In this sense, VGG-16 stood out for achieving an accuracy of 94%, while Inception V3, RestNet-50, and InceptionResNet obtained 91%, 58%, and 92% respectively. Conclusions and future work are detailed at the end of the paper.