The evolving cybersecurity landscape demands advanced malware classification techniques to mitigate risks such as data breaches and financial losses. In response, we propose a novel lightweight image-based malware classification method that leverages grayscale image transformation, making it compatible with convolutional neural networks (CNNs). Our optimized CNN model, enhanced with an attention mechanism and efficient convolutional units, effectively captures complex malware patterns and outperforms traditional classifiers. We evaluated our approach on both a curated malware dataset and a widely used open-source dataset, achieving classification accuracies of 98.71% and 98.35%, respectively, demonstrating its high efficacy and strong potential for real-world deployment. To further enhance model interpretability, we applied Grad-CAM, an explainable AI technique, which provides visual insights into the model’s decision-making process by highlighting critical regions of the input image.