The development of high-fidelity wildfire spread models is contingent upon the availability of spatially and temporally aligned multi-layer datasets. Existing global and continental databases have successfully catalogued fire events; however, they are often constrained by coarse spatial resolutions or absence of environmental variables. Additionally, there is a significant scarcity of machine-learning-ready open-access datasets dedicated to the Canadian landscape. This leaves a critical gap in the data available for modelling fire-front dynamics characteristic of Canadian ecosystems. In this paper, we introduce a comprehensive, multi-layer wildfire perimeter prediction dataset engineered specifically for the Canadian boreal forest, covering the period from 2001 to 2020. Unlike previous catalogues, this dataset includes 2,565 distinct fire events with a minimum area threshold of 1 ha (0.01 km²), capturing a more inclusive historical record of fire activity. The curation process involves a rigorous integration of final burned geometries from the Canadian National Fire Database (CNFDB) with fire-adapted environmental covariates, including high-frequency meteorological indices, static topographical features, and fuel parameters. To facilitate machine learning applications, the data is processed into a format suitable for tasks such as fire segmentation and perimeter prediction, with spatial resolutions adapted to the scale of individual fire events. By providing granular inputs for both small-scale ignitions and complex fire perimeters, this dataset serves as a foundational resource for advancing predictive modelling and real-time surveillance pipelines in northern forest environments.