<p>The Multi-Agent Path Finding (MAPF) problem involves the task of finding paths for multiple agents that want to reach their destinations without obstructing other agents. Although MAPF is essential for numerous real-world applications, finding an optimal solution to this problem is NP-hard. Many approaches have been proposed in the literature, offering sub-optimal solutions to improve runtime efficiency. <i>Lazy Constraints Addition search for MAPF (LaCAM)</i> is a state-of-the-art sub-optimal MAPF algorithm that employs tree-based lazy successor generation to minimize planning effort. However, the success of the algorithm heavily relies on the effective selection of nodes for expansion. LaCAM employs a fixed heuristic throughout the entire search process, disregarding the agents’ preferences or characteristics of the underlying environment. Nevertheless, experiments with various heuristics indicate that no single heuristic consistently outperforms others across all scenarios. Consequently, in diverse environments, as the number of agents increases, reliance on a single, general heuristic leads to diminished runtime performance. Against this backdrop, with the intent to further speed up the runtime, we propose a novel approach, called eLaCAM, that adaptively selects nodes during the search process considering the current scenario of the environment and agents preferences. We introduce two distinct variants of eLaCAM. The first, eLaCAM-stat, statistically analyses previous results of using different heuristics and selects nodes accordingly. The second variant, eLaCAM-ML, analyze the environment by extracting necessary features to guide a machine learning framework in assisting adaptive node selection during the search process. Our extensive empirical results illustrate a notable improvement in runtime and a reduction in the search space compared to state-of-the-art MAPF algorithms.</p>