Lysine methylation is a crucial posttranslational modification influencing both histone and nonhistone protein functions. Disruptions in lysine methyltransferase activity are linked to numerous diseases, including various cancers and developmental disorders. Accurate identification of lysine methylation sites is essential for early diagnosis and therapeutic development. Here, we present CNN-Meth, a newly developed Web-based utility that employs a convolutional neural network (CNN) to predict lysine methylation sites. CNN-Meth leverages evolutionary, structural, and physicochemical data alongside binary encoding for its training process. Evolutionary and structural features used to build CNN-Meth are extracted using protein modeling, which works similarly to using Protein Language Models (PLM). Unlike traditional approaches that rely on manually extracted features, CNN-Meth uses CNNs for automated feature extraction, ensuring minimal information loss. This novel methodology enhances prediction accuracy, achieving 96.0% Accuracy, 85.1% Sensitivity, 96.4% Specificity, and a Matthew’s Correlation Coefficient (MCC) of 0.65. This demonstrates the possible effectiveness of using PLM to predict Methylation sites as a future direction. The CNN-Meth tool and its source code are readily accessible at https://github.com/MLBC-lab/CNN-Meth , providing a robust resource for researchers and clinicians.