This study presents a comprehensive nonlinear finite element study on the behavior of circular fibre reinforced polymer (FRP) confined reinforced and plain concrete columns under concentric loads. For this investigation, 65 test models with a combination of spiral hoop reinforced concrete, concrete with longitudinal and circular hoop reinforcements, and FRP confined plain concrete were designed . Four different machine learning (ML) techniques were developed to predict the ultimate axial load and strain at the tensile rupture of FRP. The accuracy of the proposed finite element model (FEM) was verified by comparing it with the existing experimental test results. The impact of unconfined concrete strength, hoop reinforcement ratio, thickness of FRP, and spiral hoop spacing on the confinement effectiveness, load-carrying capacity, and ductility behavior of circular FRP confined concrete columns were demonstrated. The parametric analysis found that axial load capacity of FRP-confined concrete columns increased when unconfined concrete strength increased, while low-strength confined concrete achieved a larger strength improvement ratio than high-grade concrete. The investigation also revealed that the thickness of the confining FRP has a significant impact on the confinement effectiveness of hoop reinforcement. The correlation between the FEM and experimental tests yielded 99.60% R2 for ultimate axial load and 93.40% R2 for ultimate strain. Extra tree regressor (ETR) and gradient boosting of ML yielded accurate predictions of ultimate axial load and strain at the tensile rupture of FRP compared to other approaches, but ETR has the best comprehensive prediction performance using the comprehensive ranking system. Overall, ETR can be applied in the ultimate axial load and strain prediction of circular FRP confined reinforced and plain concrete columns under concentric loads, conserving resources, time, and cost through laboratory testing.