Diclofenac (DCF) is frequently detected in aquatic environments, emphasizing the critical need for its efficient removal globally. Here, we present the synthesis of Fe(III)-doped β-CD-grafted chitosan (Fe/β-CD@CS) cryogel beads designed for adsorbing DCF in aqueous solutions. The beads exhibited an average size of 2.94 ± 0.66 mm and a point of zero charge of 8.03. Adsorption experiments demonstrated that the Langmuir kinetic model provided the most accurate description of the kinetic data, while the Redlich–Peterson isotherm offered the best fit for the equilibrium data. The beads showcased a theoretical maximum adsorption capacity of 712.3 mg/g for DCF, with the adsorption process being identified as exothermic. DCF adsorption on the beads was attributed to hydrogen bonding, metal cation-π interactions, and electrostatic interactions. Reusability tests exhibited that the beads could be regenerated using 0.1 M NaOH. To perform deep learning modeling, adsorption experiments (n = 17), designed utilizing central composite design (CCD), were conducted in duplicate. The CCD framework incorporated input variables such as initial DCF concentration, adsorbent dosage, and solution pH, while the output variable was the DCF removal rate. Utilizing the adsorption data, an artificial neural network (ANN) model was constructed with a topology of 3: 7:10:1, featuring 3 input variables, 7 neurons in the first hidden layer, 10 neurons in the second layer, and 1 output variable. Employing the ANN model data, 3-D response surface plots were generated to elucidate the relationship between input variables and DCF removal rate. Additional adsorption tests were conducted to evaluate the developed ANN model, affirming its reliable predictability for the DCF removal rate. Analysis of the relative importance of the input variables revealed the following order of importance: solution pH (100 %) > adsorbent dosage (75.2 %) > initial DCF concentration (57.7 %).