Humpback whale (Megaptera novaeangliae) populations have rebounded significantly since the cessation of commercial whaling; however, they now face emerging threats from ocean warming and shifts in habitat suitability that affect food availability. There remains uncertainty over whether opportunistic observations can yield reliable species distribution models (SDMs) to effectively guide conservation management. This study aims to compare SDMs for humpback whales in the Great Barrier Reef Marine Park derived from different opportunistic sightings datasets and to evaluate how varying data sources influence our understanding of humpback whale habitat relationships. Using Maxent, predictive models were developed based on sighting data from citizen science initiatives and other opportunistic databases. Models were compared to assess disparities in predictive capabilities and to determine the influence of data source variability on model outcomes. Distinct environmental variables—especially bathymetry and distance to the coast—emerged as the most influential predictors. The best-performing model diverged from previous models, forecasting humpback whale distributions closer to shore, with the highest habitat suitability concentrated along the north-eastern coastal region. This study demonstrates that, when applied carefully, opportunistic data can enhance SDMs and provide valuable insights for conservation management, ultimately informing strategies to protect humpback whale populations amid changing environmental conditions.