This paper examines the use of entropy estimation and Fourier transform as feature extraction techniques for classifying cryptosystems. We evaluated four machine learning (ML) models—Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT)—across six widely used block ciphers. The experimental results show that RF achieved an accuracy of 97–99%, significantly surpassing previous benchmarks of 52.5–65%. SVMs demonstrated strong performance as well, with precision rates of 93–97% and recall rates of 92–96%. We also emphasize the potential of these feature extraction methods for real-time cryptographic analysis, despite their high computational demands. These findings highlight the importance of model optimization and feature selection in enhancing cryptosystem identification for real-time applications, contributing to cryptographic security through efficient ML techniques suitable for high-speed, large-scale environments.