Malaria remains a major global health challenge, particularly in tropical regions where timely diagnosis is critical. Traditional diagnostic methods, such as microscopic examination of blood smears, are time-consuming and require expert interpretation. This study proposes a deep learning framework based on Convolutional Neural Networks for automated malaria detection using microscopic cell images. Trained on the publicly available Malaria Cell Images Dataset, the proposed model eliminates the need for manual feature engineering by leveraging hierarchical feature extraction. Experimental evaluation demonstrates a classification accuracy of 96.36%, with high sensitivity and specificity. These results highlight the potential of CNNs as scalable, low-cost diagnostic tools suitable for deployment in resource-constrained settings, contributing to the advancement of AI-assisted medical diagnostics.