Malware is a rapidly increasing menace to modern computing. Malware authors continually incorporate various sophisticated features like code obfuscations to create malware variants and elude detection by existing malware detection systems. The classification of unseen malware variants with similar characteristics into their respective families is a significant challenge, even if the classifier is trained with known variants belonging to the same family. The identification and extraction of distinct features for each malware is another issue for generalizing the malware detection system. Features that contribute to the generalization capability of the classifier are difficult to be engineered with modifications in each malware. Conventional malware detection systems employ static signature-based methods and dynamic behavior-based methods, which are inefficient in analyzing and detecting advanced and zero-day malware. To address these issues, this work employs a visualization approach where malware is represented as 2D images and proposes a robust machine learning-based anti-malware solution. The proposed system is based on a layered ensemble approach that mimics the key characteristics of deep learning techniques but performs better than the latter. The proposed system does not require hyperparameter tuning or backpropagation and works with reduced model complexity. The proposed model outperformed other state-of-the-art techniques with a detection rate of 98.65%, 97.2%, and 97.43% for Malimg, BIG 2015, and MaleVis malware datasets, respectively. The results demonstrate that the proposed solution is effective in identifying new and advanced malware due to its diverse features.