Variable Neighborhood Search (VNS) is a prominent metaheuristic for solving combinatorial optimization problems. While adaptive strategies have been explored to enhance VNS, identifying effective criteria for adaptive decision making remains a challenge. This paper introduces an adaptive VNS variant that leverages Fitness Landscape (FL) Analysis to guide neighborhood selection. Our method uses machine learning to determine the best neighborhood structure for each iteration, analyzing key features of the fitness landscape. It evaluates the importance of these features using Shapley values, a technique from explainable artificial intelligence. The effectiveness of the proposed method is validated through experiments on benchmark instances of the Traveling Salesman Problem (TSP), demonstrating superior solution quality compared to traditional VNS and competitive performance relative to other metaheuristics. This study underscores the potential of landscape-aware optimization to enhance the metaheuristic performance for complex combinatorial problems while providing valuable insights into the influence of key landscape features on adaptive search strategies.