Permeability modelling remains a major challenge in the reservoir modelling exercise. The main reason for this is the limited availability of measured input data and the effect of different geological processes on reservoir permeability. This leads to nonrepresentation of high-permeability streaks in the model. In this paper, we present a machine-learning (ML) driven approach that captures the permeability variation in the reservoir using available input data. In ML, clustering is an unsupervised approach aimed at automatically grouping data with similar properties. We use several clustering techniques to automatically identify high-permeability data points by dividing data into groups, also known as clusters, and then choosing the cluster with the maximum permeability and assigning it a new rock type. For each rock type, we fit and evaluate many ML regression models, and show their outperformance over traditional fitting approaches. Porosity and several openhole log properties are used as input for the regression models. By fixing porosity but varying the other properties, the variability of permeability values is predicted. Clustering, using ‘K-Means’ ML algorithm, resulted in an efficient approach of automated high permeability identification. Several ML models were trained and evaluated, and the models with the minimum error scores, namely mean square error (MSE) and R Squared (R2 ), were chosen for further predictions. Random Forest was within the top models for a variety of rock types. In general, complex curve fitting using ML outperformed traditional fitting approaches (i.e., straight line fitting) and demonstrated high potential for accurate, automated, high-permeability identification and integration. The predicted permeability has been calibrated with well test permeability data.