This study optimizes green porous oil adsorbent material design and selection to improve adsorption efficiency, cost-effectiveness, and sustainability to address the growing environmental challenge of oil spill remediation. It examined 50 green porous adsorbent materials including key properties and performance metrics. Material development is systematic to improve oil spill cleanup solutions' scalability, performance, and environmental impact. Experimental optimization, computational modeling, machine learning prediction, and multi-criteria decision analysis used for high-performance oil spill adsorbents. The surface area, pore size, surface functionalization, and hydrophobicity index of green porous adsorbents were examined. Multiphysics (v5.6, Subsurface Flow Module) and ANSYS Simulated oil-water adsorption in fluid porous media. For multiphysics coupling flexibility and porous structure transport modeling, COMSOL that simulate oil-water separation processes under various operational conditions. COMSOL Multiphysics and ANSYS Fluent modeled flow dynamics and adsorption in porous media with experimental optimization. Based on material properties, artificial neural networks and random forests were trained on experimental and simulated data to predict adsorption capacities and reveal adsorbent material behavior under different conditions. Under operational conditions, the integrated framework optimized material properties to improve adsorption efficiency. Machine learning and modeling predicted material behavior, while decision analysis made selection objective and transparent. This scalable, data-driven optimization of adsorbent materials helps academia and industry develop and deploy oil spill remediation solutions faster. It emphasises integrating experimental, computational, predictive, and decision-making methods for oil spill remediation and other environmental and industrial material optimisation problems.