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Risky projects such as tall buildings have suffered an alarming rate of increase in delays and total abandonment. Though numerous delay studies predominate, what is lacking is constructive research to develop tools and techniques to wrestle the inherent problem. Consequently, this paper presents the development of a machine learning model for delay risk assessment in tall building projects. Initially, 36 delay risk factors were identified from previous literature, and subsequently developed into surveys to determine the likelihood and consequence of the risk factors. Forty-eight useable responses obtained from subject matter experts were used to develop a dataset suitable for machine learning application. K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble methods were considered. Feature subset selection revealed that the most relevant independent …