Nowadays, the challenge of industrialising concrete is to produce economical, ecological and efficient concrete. As a result, cementitious additions are increasingly used in concrete binders with proportions conferring the desired properties, particularly compressive strength. Palm oil fuel ash (POFA) from palm trees is one of these additions and introduced by substitution of cement in concrete. Although laboratory tests applied to concrete samples are expensive and time-consuming, considerable research is directed towards modelling approaches and machine learning (ML) applications. The purpose of this study is to explore different ML techniques, including artificial neural network (ANN), ANN with combined inputs (ANNX), particle swarm optimisation (PSO) and genetic algorithm (GA). These techniques are used to predict the compressive strength of POFA concrete derived from a dataset consisting of 249 samples with regard to 6 input parameters, namely, cement and POFA contents, aggregate ratio, water-to-binder ratio, superplasticiser dosage and curing duration. The performance of these techniques is evaluated using six performance indices: correlation coefficient, Willmott index of agreement, Nash–Sutcliffe efficiency, root mean squared error, mean absolute error and mean squared relative error. The comparison shows that ANN and ANNX have better predictive accuracy than PSO and GA, and ANNX performs relatively better than ANN. Results are presented using a 3D surface response plot which visually depicts the relationship between predicted outcomes and influential parameters. The comparison made between ANNX and other models in the literature demonstrated that the present model is reliable to predict CS of POFA concrete with high precision in a wide range of data.