In this study, a binary composite adsorbent based on activated carbon and phosphoric acid geopolymer foam (ACP) was prepared by combining phosphoric acid geopolymer (PAGP) with activated carbon (AC) and applied for the removal of methylene blue (MB). Activated carbon was thoroughly mixed with a mixture of fly ash and metakaolin in varying ratios, followed by phosphoric acid activation and thermal curing. The ACP adsorbent was characterized using scanning electron microscope (SEM), Fourier transform infrared (FTIR) spectrophotometer, X-ray diffractometer (XRD), surface area analyser (SAP), and thermogravimetric analyser (TGA). Batch analysis was performed to examine the effects of various adsorption parameters including pH (2, 4, 6, 7, 8, and 10), adsorbent dosage (0.06-0.2 g), MB concentration (50-250 mg L^-1), contact duration (up to 240 minutes), and temperature (25-55 °C). The ACP with 70% PAGP and 30% AC was found to be the most suitable adsorbent as it maintained its structure and exhibited better MB adsorption. The ACP had a surface area of 47.36 m² g^-1 and a pore size of 5.6 nm and was found to be amorphous in nature. The adsorption equilibrium reached in 240 minutes at pH 7, indicating an efficient adsorption process. The adsorption increased with the initial dye concentration and decreased with the increase in temperature. The ideal parameters for adsorption of MB using ACP include 0.2 g of adsorbent, 25 °C, pH 10, and 240 minutes. The adsorption data fitted well with the Langmuir isotherm, pseudo-second-order (PSO) kinetics model, and three-step intraparticle diffusion (IPD) model. The adsorption capacity calculated using the Langmuir isotherm was 204.8 mg g^-1 with an R ² = 0.989. Thermodynamics parameters showed that the adsorption process was exothermic, energetically favourable, and associated with a decrease in entropy. According to the FTIR findings, pH effect, Langmuir isotherm, PSO kinetics, IPD model, and thermodynamics factors, chemisorption is identified as the predominant process. Different machine learning models, i.e., gaussian process regression (GPR), support vector regression (SVR and SVR-rbf), random forest regression (RFR), decision tree regression (DTR) and artificial neural network (ANN), were trained and tested using adsorption capacity and % removal data. The ANN model (random search) demonstrated better performance compared to other models, achieving an R ² value of 0.873 for adsorption capacity and 0.799 for % removal on test data.

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