Deep Learning has developed into a significant tool across multiple applications, achieving broad acceptance compared to conventional machine learning models. The application of deep learning algorithms, specifically Convolutional Neural Networks (CNN), has markedly progressed the medical domain, where extensive quantities of images necessitate detailed processing and analysis. This document presents the development of a deep learning model aimed at addressing the intricate challenge of blood cell classification, which is a vital component of blood diagnostics. This document outlines a framework utilizing convolutional neural networks (CNN) for the automatic classification of blood cell images into designated subtypes. Extensive experiments are performed on a dataset that includes 13,000 blood cell images along with their respective subtypes. The results indicate that the proposed model outperforms existing alternatives, exhibiting enhanced performance across multiple evaluation metrics.