Monkeypox has recently emerged as a public health emergency with rising cases worldwide. Early clinical diagnosis is challenging due to symptom overlap with other diseases, but characteristic skin lesions provide distinguishing visual cues. This work proposes a deep convolutional neural network (CNN) tailored for automated monkeypox screening from lesion images. A dataset of over 3000 dermatological images was compiled, with data augmentation to enhance diversity. The CNN architecture comprised convolutional blocks for feature extraction and dense layers for classification. Rigorous training and cross-validation were conducted over 100 epochs to optimize model performance. On an unseen test set, the model achieved 86.87\% accuracy in classifying monkeypox lesions, with 94\% precision, 79\% recall and 86\% F1-score. These metrics were better than baseline models, indicating reliable screening potential. Though the model overlooked some atypical presentations, successes showcase utility for mass case-finding. As monkeypox monitoring intensifies, robust computer vision approaches can assist clinicians through explainable, real-time forecasts. Prospective validation across demographics and integration with clinical workflows is warranted before full-scale deployment. Overall, the study demonstrates deep learning’s promise in tackling the monkeypox outbreak through enhanced diagnosis.