The number of malware extending along through World Wide Web and endangering computer system as well as other internet connections has drastically risen over the past few years. To deadline, multiple methods and approaches for detecting and minimizing these malware entities have been suggested. Nevertheless, as innovative and integrated malware strategies arise, a large amount of malware is formed that can circumvent a variety of advanced malware tracking systems. As an outcome, in an effort to safeguard information from harmful activity, this article suggests an image-based malware classification utilizing Deep Learning (DL) techniques. To facilitate the process, the input dataset is pre-processed using the Keras pre-processing method. Data preparation and data processing are two aspects of data pre-processing. The preprocessed data is fed into the data visualization block after pre-processing. These visualizations aid in determining how data is handled as well as possible irregularities in the data. Finally, for improved accuracy, the convolution neural network (CNN) DL classification method is used. The obtained outcomes are validated using python platform. The proposed CNN attained high accuracy in contrasted with other approaches.