As stuck pipe continues to be a major contributor to non-productive time (NPT) in drilling for oil and gas operations, efforts to mitigate its incidence cannot be over emphasized. A machine learning approach is presented in this paper to identify warning signals and give early indications for an impending stuck pipe possibility during drilling activities so as to take proactive measures to mitigate its occurrence. The model uses a moving window-based approach to capture key drilling parameters trends and apply an unsupervised machine learning algorithm to predict abnormalities in the parameters’ rate of change. It utilizes most commonly available drilling real-time data and is therefore deployable in all type of wells. No pre-drill model is essentially required as the model utilizes a self-learning and self-adjusting model. The methodology involves the use of change point detection in identifying rig activity and the associated drilling parameters so as to capture relevant parametric trend for analysis. Inherent in the parameter trend are the different factors that affects their readings; such as wellbore geometry, bottom-hole assembly (BHA), dogleg severity (DLS), formation characteristics, pump flow rate and pipe rotations. The algorithm has been tested on historical wells data in which stuck pipe incidence, near-miss stuck pipe occurred, and incidence-free wells to prove the concept. The results of the model performance is hereby presented along with an accuracy measure.