As Unmanned Aerial Vehicles (UAVs) are becoming crucial in modern warfare, research on autonomous path planning is becoming increasingly important. The conflicting nature of the optimization objectives characterizes path planning as a multi-objective optimization problem. Current research has predominantly focused on developing new optimization algorithms. Although being able to find the mathematical optimum is important, one also needs to ensure this optimum aligns with the decision-maker's (DM's) most preferred solution (MPS). In particular, to align these, one needs to handle the DM's preferences on the relative importance of each optimization objective. This paper provides a comprehensive overview of all preference handling techniques employed in the military UAV path planning literature over the last two decades. It shows that most of the literature handles preferences by the overly simplistic method of scalarization via weighted sum. Additionally, the current literature neglects to evaluate the performance (e.g. cognitive validity and modeling accuracy) of the chosen preference handling technique. To aid future researchers handle preferences, we discuss each employed preference handling technique, their implications, advantages, and disadvantages in detail. Finally, we identify several directions for future research, mainly related to aligning the mathematical optimum to the MPS.