Effective removal and optimization models of metolachlor (MET) adsorption was carried out using MIL-53(Al) metal�organic framework (MOF), response surface methodology (RSM), artificial neural network (ANN) and molecular docking simulation. The MOF was hydrothermally synthesized and characterized using the Field Emission Scanning Electron Microscopy (FESEM), Powdered X-ray diffraction (XRD), Fourier Transformed Infrared (FTIR) and Brunauer, Emmett and Teller (BET). High adsorption capacity of MET was recorded (241.617 mg/g) with fast equilibration time of � 25 min at 0.999 coefficient of determination (R2) which was due to the high surface area of the MOF (1104 m2/g). The kinetics followed the pseudo-second order model describing a chemisorption adsorption process. The isotherm showed a multilayer adsorption process of the Freundlich model with R2 of 0.990. The thermodynamic parameters followed an endothermic and spontaneous adsorption. The optimization by the RSM was significant with minimum number of experimental runs, lesser error and showed a simultaneous interaction of the adsorption parameters in predicting MET adsorption capacity. The ANN learning algorithm performed significantly in predicting and validating the experimental conditions designed by the RSM model. Both the RSM and ANN model provide remarkable values that are very similar with the experimental results. The binding mode and binding affinity of MIL-53(Al) and MET were determined by the docking simulation. Prospect for the reusability potential of the MOF was explored and the findings were significant. Hence, the results showed the potential of the MOF for the effective remediation of MET in aqueous medium. © 2022 Elsevier B.V.