ABSTRACT To address the issues of low tracking accuracy and smoothness in autonomous navigation on complex paths for forestry mobile platforms, this paper presents an improved snake optimizer (ISO) as the core innovation for optimizing pure pursuit path tracking. The ISO introduces three key algorithmic innovations: (1) a spatial pyramid matching (SPM) chaotic map to improve initial population uniformity and accelerate convergence; (2) a dynamic search factor to prevent local optima during the foraging phase; and (3) restricted opposition‐based learning to enhance global search capability. This improved optimizer is integrated with pure pursuit (ISO‐PP) through a deviation prediction model that forecasts lateral and heading deviations under different look‐ahead distances. The ISO evaluates these predicted deviations to select optimal look‐ahead distances in real‐time. Experimental validation on forestry U‐turn and shuttle paths confirmed ISO‐PP's statistically superior performance. Compared to the optimal fixed look‐ahead distance pure pursuit algorithm, ISO‐PP reduced mean lateral deviations by 41.7% and 30.6%, maximum lateral deviations by 44.7% and 37.4%, curvature standard deviation by 60.0% and 60.2%, and heading deviation standard deviation by 5.6% and 23.0%. It also outperformed the pre‐improvement SO‐PP algorithm with up to 29.4% reduction in mean lateral deviation. The proposed ISO algorithm effectively enhances tracking accuracy and smoothness. It outperforms other look‐ahead distance adaptation methods reported in the literature, providing a novel and superior optimization approach for autonomous forestry navigation.