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The 2023 wildfires caused serious riks to air quality and public health across various provinces in Canada, particularly during the period from May to June. This study aims to systematically evaluate the impact of the May-June wildfire events on air pollution in Canada using satellite Aerosol Optical Depth (AOD) data, along with the Hybrid Single-Particle Lagrangian Integrated Trajectory model and a Bayesian spatiotemporal dynamic model to investigate the distribution, transport pathways, and complex meteorological drivers. Results indicate that the AOD data in the Canadian region is in excellent agreement with the ground-based AOD observation (R > 0.90 at 12 of 14 sites). The highest aerosol concentrations were centered over western and central provinces during the wildfire season, with long-range eastward transport. Quebec (26%) and Alberta (25%) constituted over half of the national total for severe pollution events, while the Northwest Territories experienced the most prolonged exposure, with 45% of its remote communities severely affected. The temperature lag and wind speed are the dominant factors and exhibited highly dynamic roles during four main periods: wind speed's function shifted from a local dispersant (WS = -0.419) to a regional transporter (WS1 = 0.123, one-day lagged) depending on fire source configuration, while temperature's influence ranged from enhancing dispersion (T1 = -0.634, one-day lagged) to causing extreme pollutant trapping under heat dome conditions (T1 = 1.273).