In recent years, modulation type recognition has garnered a lot of attention across the board. New methods for automatically recognizing digital modulations are presented in this research. Digital modulations like Frequency Shift Keying and Amplitude Shift Keying are two of the most often seen types that we investigate. These modulations may be detected using a Signal Noise Rate (SNR) value ranging from10 to +25. Once this is done, the modulation type may be determined with the use of machine learning algorithms such as Random Forest, Support Vector Machine, and Naive Bayes. The data suggest that the signal noise rate has an influence on identification accuracy

Digital Modulation, FSK, ASK, SNR, Machine Learning.