A bio-composite represents a unique material composition comprising either two or more elements, and selecting one that originates from natural sources. While significant research has delved into investigating the wear tendencies and mechanical attributes of polymer composites reinforced by various natural fibers and fillers, there is a notable gap in exploring the integration of Madhuca Longifolia (ML) dry leaf powder within polyester composites. This research initiative undertakes a comparative evaluation focused on the dry sliding wear behavior of ML dry leaf powder. Through the formulation of composites incorporating dry leaf particles sized at 400 μm, with weight fractions of 5% and 10%, the fabrication process employs the hand molding technique. Wear assessments were undertaken utilizing pins measuring 10 mm in diameter and 80 mm in length. The wear trials encompassed a range of parameters, including velocities spanning 200–800 revolutions per minute, standard loads varying from 5 to 25 N, fiber concentrations from 0 to 10 wt%, and sliding distances between 1 and 4 km. The minimum specific wear rate is observed to be 2.7422 mm/N-m for 5 wt% of filler loading. The study systematically explores the wear attributes of the developed materials employing the Taguchi design-of-experiments methodology. Furthermore, the wear data is meticulously analyzed through the utilization of the artificial neural network (ANN) technique to authenticate the findings derived from the study. The ANN is an advanced engineering technique that utilizes its unique characteristics of being able to study data patterns. It is widely used in industrial applications that use machine learning techniques, is a key-stone in the trending Industry 4.0, and is a great tool for refining data. The minimum and maximum prediction error using ANN is observed to be 0.4833% and 8.8921%, respectively.