There are various scenarios, whether they be commercial or defense, where privacy is important. In communications, the metrics of low probability of interception is often used to measure the signal's ability to resist interception and decoding by unauthorized parties. Joint radar sensing and communications (RadarCom) has been of interest recently and an important requirement of RadarCom signals is its immunity to interceptions. In this context it is of interest to understand the statistics of background clutter. This paper uses machine learning (ML) approaches to classify and model clutter in presence of noise/interference. We employ 32 sub-carrier orthogonal frequency division multiplexing waveforms as a basis for clutter return collection and subsequent use as RadarCom signals. We then present the ML combination method with the best classification accuracy of 78.9%.