This paper investigates the development and application of adaptive Variable Neighborhood Search (VNS) heuristics for solving complex optimization problems. Through a systematic literature review of studies from the past decade, the research aims to identify how adaptive mechanisms have been integrated into VNS methods, the learning strategies employed, and the specific VNS variants enhanced by these adaptations. The review reveals that most studies have focused on improving the intensification phase of the Basic VNS (BVNS) using probabilistic-based learning strategies. However, there is a noticeable gap in understanding the overall impact of these adaptive mechanisms on the performance of VNS algorithms. Future research should explore alternative VNS variants and develop more comprehensive adaptive strategies that simultaneously address multiple phases of the VNS process. This approach could lead to more balanced and effective optimization methods. The findings suggest that, while significant progress has been made, further investigation is needed to fully understand and leverage the potential of adaptive VNS heuristics.