As large carnivore populations expand across human-dominated landscapes worldwide, there is a growing need to assess their medium- and long-term conservation outlook and their potential consequences for human activities. The wolf (Canis lupus) recolonizing the Italian Alps is a good case study. This species of high ecological and societal interest has recently seen its legislative protection status revised, a change driven by its continued expansion and the resulting socio-economic challenges. Its recovery must be assessed against Favourable Conservation Status, an ambitious concept embedded in the Habitats Directive that requires demonstrating a favourable long-term viability of the population. Such predictions are particularly challenging, as wolf recolonization is governed by pack-level social processes, significant habitat selection, and, critically, stochastic long-distance dispersal events that cannot be anticipated. To address this, we developed a spatially explicit individual-based model based on twenty years of population-level data. The model is adaptive, periodically realigning the simulated population to the documented one so that projections remain anchored to the observed trajectory of recolonization and to documented stochastic events. Within a pattern-oriented hybrid framework coupling the model with a neural-network emulator, we calibrated hard-to-observe processes such as dispersal, together with novel processes arising as the population in some areas evolves to high density, such as density dependence. We then validated the model against independent spatial patterns across successive time intervals, projecting pack distribution and population expansion forward in time within known uncertainty bounds. Exploring the projected expansion potential across the Alps, this model can inform the species' progress toward Favourable Conservation Status while also evaluating the evolving overlap between wolf and human activities. By running the model across a range of scenarios, this framework can support management and conservation decisions for a recovering, wide-ranging population.