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Unmanned aerial vehicle (UAV) detection issues can be successfully resolved by machine learning (ML) algorithms effectively. This research presents a developing ML approach called convolutional neural network (CNN) models for the accurate categorization of Micro UAVs, leveraging current breakthroughs in ML technology. To improve computing efficiency over RGB channels, the classification process entails extracting radio frequency (RF) data from several drones and expressing them using grayscale values. We use the DroneRC dataset for this simulation study. Raw RF data is preprocessed using the Short-Time Fourier Transform and the power spectral density technique to extract the most pertinent properties before the ML models are trained. The outcomes of the simulations show that the suggested machine learning models attain a high degree of classification accuracy while minimizing errors during …