Conventional methods for removing lead (II) from wastewater are often inefficient and environmentally unsustainable, necessitating the development of predictive, eco-friendly adsorption models using biodegradable materials. This study investigated the performance of a laboratory-scale adsorption system for removing lead (II) ions from aqueous solutions via a calcium–alginate–cellulose nanocrystal composite. The adsorption process was modelled and optimized via the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), two soft computing tools. The operational parameters, namely, pH, initial concentration, contact time, and adsorbent dosage, were evaluated to determine their influence on the adsorption efficiency. Predictive models were developed and validated for lead (II) removal, achieving high determination coefficients (R2) of 0.982 for ANN and 0.995 for ANFIS. Comparative statistical analyses, including MSE (0.003 for ANN, 0.001 for ANFIS), HYBRID (0.292 for ANN, 0.126 for ANFIS), χ2 (0.075 for ANN, 0.038 for ANFIS), ARE (0.002 for ANN, 0.000 for ANFIS), MPSD (0.598 for ANN, 0.298 for ANFIS), and SSE (0.070 for ANN, 0.053 for ANFIS), confirmed that both models provided accurate predictions, with ANFIS exhibiting superior performance. These findings demonstrate that soft computing techniques, particularly ANFIS, offer reliable tools for predicting and optimizing heavy metal removal via biodegradable adsorbents, advancing sustainable wastewater treatment strategies.