Finding a feasible path for an unmanned aerial vehicle (UAV) in a complex environment is a crucial part of any UAV mission planning system. Many algorithms have been developed to identify optimal or nearly optimal pathways for UAVs; however, the vast majority of those algorithms do not deal with this problem as multiobjective. Therefore, this study is presented to propose a new multiobjective optimization technique, namely the hybrid slime mould algorithm (HSMA), based on hybridizing the slime mould algorithm with a new updating mechanism to strengthen its performance when applied to tackle the multiobjective path planning problem in 3-D space. This algorithm employs Pareto optimality to tradeoff between various objectives. Those objectives include path optimality for minimizing the fuel cost and consumed time to reach the target location, flying away from threats to ensure safe operation, and finally the smooth cost to assess the climbing and turning rates. HSMA was evaluated using six benchmarking scenarios with various difficulty levels and compared to several recently published and well-established algorithms to show its effectiveness for several performance metrics, such as the convergence curve, Wilcoxon rank-sum test, and inverted generational distance metric. The experimental findings expose that HSMA is more effective than all the compared optimizers in terms of all performance metrics. Hence, it is the best alternative for efficiently creating high-quality pathways for UAVs.