The conventional drilling process is a critical machining operation widely used in manufacturing, where the optimization of process parameters plays a significant role in enhancing efficiency, product quality, and tool longevity. This paper explores the application of human-inspired metaheuristic algorithms for the parametric optimization of conventional drilling operations. Human-based metaheuristics, inspired by human behaviors and decision-making processes, such as teamwork optimization algorithm (TOA), teaching learning-based optimization (TLBO), search and rescue optimization (SAR), human conception optimizer (HCO), and queuing search algorithm (QSA), provide innovative and adaptable strategies for solving complex optimization problems. For the example of the drilling process of polymer nano-composites, TLBO outperforms other considered algorithms and their optimization performance is compared concerning solution accuracy, variability, and computational effort. It provides improvements of 2.93, 1.18, and 37.35% for single objective optimization; and 2.39, 0.49, and 11.69% for multi-objective optimization, the optimal machining parameters’ setting for minimum delamination factor at entry and ex it and thrust force respectively against the observations of the past researchers. Metaheuristic algorithms are optimized by multi-objective optimization, leading to the computation of a Pareto optimum front that includes the optimized replies. Results of two quality metrics (spacing and hypervolume) and non-parametric statistical tests (Friedman’s mean rank test) also prove the superiority of TLBO against the other human-inspired algorithms under consideration.