Abstract Boreal forests are a key component of the global carbon cycle, forming North America's most extensive biome. Different successional stages in boreal forests have varying levels of ecological values and biodiversity, which in turn affect their functions. A knowledge gap remains concerning the present successional stages, their geographic patterns and possible successions. This study develops a novel application of UAV‐LiDAR and Red Green Blue (RGB) data and network analysis to enhance our understanding of boreal forest succession. Between 2022 and 2024, we collected UAV‐LiDAR and RGB data from 48 forested sites in Alaska and Northwest Canada to (i) identify present successional stages and (ii) deepen our understanding of successional trajectories. We first applied UAV‐derived spectral and structural tree attributes to classify individual trees into plant functional types representative of boreal forest succession, amely, evergreen and deciduous . Second, we built a forest‐patch network to characterize successional stages and their interactions and assessed future stage transitions. Finally, we applied a simplified forward model to predict future dynamics and highlight different successional trajectories. Our results indicate that tree height and spectral variables are the most influential predictors of plant functional type in random forest algorithms, and high overall accuracies were attained. The network‐based community detection algorithm reveals five interconnected successional stages that could be interpreted as ranging from early to late successional and a disturbed stage. We find that disturbed sites are mainly located in Interior and Southcentral Alaska, while late successional sites are predominant in the southern Canadian sites. Transitional stages are mainly located near the tundra‐taiga boundary. These findings highlight the critical role of disturbances, such as fire or insect outbreaks, in shaping forest succession in Alaska and Northwest Canada.