With the growing threats of keyloggers in the world, intelligent detection algorithms are important for ensuring mathematical systems. Attackers are uniformly conceiving new procedures for initiating keylogger attacks, making discovery a daunting task for protection artists. We discuss machine learning and deep learning algorithms to fight keyloggers, which steal keystrokes. Machine learning seems effective in this area, but researchers want to compare different algorithms to determine which works best. The idea is to use machine learning models to identify the hidden patterns of keyloggers. Our research focuses on comparing two specific algorithms, Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), to see how well they detect keylogger activity. The study will involve preparing data specifically for keylogger detection and then designing ANN and CNN architectures suited for this task. Finally, the models are trained, tested, and evaluated to see how well they perform in identifying keylogger behavior.