Porosity, as one of the reservoir properties, is an important parameter to numerous studies, i.e., the reservoir�s oil/gas volume estimation or even the storage capacity measurement in the Carbon Capture Storage (CCS) project. However, an approach to estimate porosity using elastic property from the inversion propagates its error, affecting the result�s accuracy. On the other hand, direct estimation from seismic data is another approach to estimating porosity, but it poses a high non-linear problem. Thus, we propose the non-parametric machine learning approach, Gaussian Process (GP), which draws distribution over the function to solve the high non-linear problem between seismic data with porosity and quantify the prediction uncertainty simultaneously. With the help of Random Forest (RF) as the feature selection method, the GP predictions show excellent results in the blind test, a well that is completely removed from the training data, and comparison with other machine learning models. The uncertainty, standard deviation from GP prediction, can act as a quantitative evaluation of the prediction result. Moreover, we generate a new attribute based on the quartile of the standard deviation to delineate the anomaly zones. High anomaly zones are highlighted and associated with high porosity from GP and low inverted P-impedance from inversion results. Thus, applying the GP using seismic data shows its potential to characterize the reservoir property spatially, and the uncertainty offers insights into quantitative and qualitative evaluation for hydrocarbon exploration and development. © The Author(s) 2024.