Optimization of chemical processing, energy systems, and environmental remediation relies on precise adsorption kinetics modeling. Predictive adsorption kinetic models are developed in this study by integrating specialized engineering tools with modern image analysis software. Batch experimental data is commonly used in traditional kinetic modeling, although it might not consider the impact of space and shape on adsorption processes. This method allows for the spatially resolved data extraction from adsorbent surfaces and porous media by integrating finite element analysis (FEA) software (e.g., COMSOL Multiphysics) with high-resolution image analysis utilities (e.g., ImageJ, MATLAB Image Processing Toolbox). To enhance the precision of mass transfer predictions, kinetic models use image-derived pore structure factors as surface area, porosity, and tortuosity. Machine learning approaches are also utilized to improve model generalizability across many materials and to associate picture attributes with kinetic constants. According to findings, the prediction capacity of Langmuir, pseudo-second-order, and intraparticle diffusion models is greatly enhanced when image-informed inputs are used. Better adsorption-based system design for environmental and industrial applications may be possible with this hybrid approach, which shows the possibility of material-specific, real-time adsorption behavior modeling.