In this work, the predictive capabilities of response surface methodology (RSM) and adaptive neuro fuzzy inference systems (ANFIS) in modeling aluminum (Al) and mild steel corrosion inhibition by Aspilia Africana (A. Africana) were comparatively analyzed. Phytochemical and Fourier Transform Infrared Spectroscopy (FTIR) characterization of A. Africana leaf extract indicated that the inhibitor possessed high value flavonoids, Tannins and dominant functional groups necessary for promoting sustainable corrosion inhibition. While statistical parameters verified the applicability of RSM and ANFIS techniques in modeling the corrosion inhibition of Al and mild steel, error indices illustrated the dominance of ANFIS (R² = 0.9917) and RSM (R² = 0.9905) in predicting the inhibition efficiency of Al and mild steel, respectively. Analysis of variance (ANOVA) showed that acid concentration (F-value = 191.23) was the most influential process parameter in the modeling of Al corrosion inhibition process, while A. Africana inhibitor concentration presented an F-value of 160.5 to maintain its superior position among other factors in the modeling of mild steel corrosion inhibition. ANFIS coupled genetic algorithm optimization (ANFIS-GA) of Al corrosion inhibition was validated to be 80% at HCl conc. of 0.7 M, inhibitor conc. of 0.59 g/L and immersion time of 6.2 h. Similarly, mild steel corrosion inhibition process was optimized using RSM-GA and validated to be 77.3% efficiency (HCl conc. = 0.5 M, inhibitor conc. = 0.37 g/L and time of 4.8 h). Post optimization characterization using electrochemical studies demonstrated close agreement with inhibition efficiency obtained by gravimetric technique. Furthermore, polarization studies indicated that A. Africana leaf extract acted as a mixed type inhibitor in the corrosion process of Al and mild steel species.