The importance of Unmanned Arial Vehicle (UAV) has made progressive usage in recent times due to ease of availability and miniaturization. While on another hand, it might pose a malicious effect on public safety, so the most important problem to be addressed is the recognition of drones in sensitive areas. This paper addressed the machine learning approach to recognize UAV through its acoustic emission using representative algorithms of Mel frequency cepstral coefficients (MFCCs) for feature extraction and random forest (RNF) classifier for classification. However, temporal and spectral features are devised to demonstrate performances of beam-formed signals (enhanced emitter at desired direction) and raw signal (captured in flying test). Results of extracted features from a beam-formed signal, demonstrate the effectiveness of MFCC performance regardless of a noisy environment with a high accuracy rate as compared to raw signal. RNF classifier was trained to classify feature vector, which is obtained from the feature extraction stage. However, the classifier helped to classify samples from a small data set with good accuracy. It can appropriately classify with a likelihood of around 75% under various training data sets.