Abstract: A computer implemented method for prediction of geomechanical performance including productivity index decline and completion integrity for a well or a hydrocarbon reservoir using a geomechanics informed machine intelligence (GIMI) algorithm. The method includes running a geomechanical reservoir simulator to generate training datasets for the hydrocarbon reservoir and incorporating physical models and identified variables into the GIMI algorithm. The method further includes training a neural network of the GIMI algorithm by using correlated training datasets that correlate to the physical models to produce a resulting prediction model and performing sensitivity analysis on the resulting prediction model. Additionally, the method includes identifying dominant variables for damage mechanisms through design of experiment statistics and performing history matching and blind test on the resulting prediction model.