<p>Sustainable coloration of cotton is challenging due to fiber’s native negative surface charge and low affinity for natural dyes. This study addresses the issue through cellulose cationization, which develops permanent positive charges on the fiber surface. This modification facilitates a strong electrostatic attraction with the anionic flavonoid chromophores found in the natural yellow dye derived from <i>Spartium junceum</i> L. flower extract. This approach enables salt-free coloration, directly supporting cleaner production. Reproducibility and process optimization are among the challenges that can be solved by using artificial intelligence. This study combined the environmentally beneficial technique of cationization with advanced artificial intelligence (AI) models, and the effects of four key processing parameters (cationizing agent concentration, dyebath pH, dyeing time, and temperature) on the color strength of cationized cotton fabric dyed using <i>Spartium junceum</i> L. flower extract were investigated. Multi-factor Analysis of Variance (M-ANOVA) confirmed that all four factors were statistically significant. To establish accurate prediction tools, the three key factors were modeled using regression, an artificial neural network (ANN), and an adaptive neuro-fuzzy inference system (ANFIS). The predictive power was significantly improved using optimization approaches such as a genetic algorithm (GA) and a non-dominated sorting genetic algorithm (NSGA-II). A single-hidden-layer ANN with 10 neurons (optimized by NSGA-II to minimize error on the test and combined training-validation groups) provided the most accurate prediction, with a Mean Absolute Percentage Error (MAPE) of less than 4%. Finally, a sensitivity analysis determined that dyeing time had the most significant impact on the final color strength.</p>