Understanding and predicting recreational angler effort is important for sustainable fisheries management. However, conventional methods of measuring angler effort, such as surveys, can be costly and limited in both time and spatial extent. Models that predict angler effort based on environmental or economic factors typically rely on revealed data (past observed behavior), which often limits their spatial and temporal generizability due to data scarcity. In this study, high-resolution data from an online fishing platform and easily accessible auxiliary data were tested to predict daily boat presence and aerial counts of boats at almost 200 lakes over 5 years in Ontario, Canada. Lake-information website visits alone enabled predicting daily angler boat presence with 78% accuracy. While incorporating additional environmental, socio-ecological, weather, and angler-reported features into machine learning models did not remarkably improve prediction performance of boat presence, they were substantial for the prediction of boat counts. Models achieved an R² of up to 0.77 at known lakes included in the model training, but they performed poorly for unknown lakes (R² = 0.21). The results demonstrate the value of integrating data from online fishing platforms into predictive models and highlight the potential of machine learning models to enhance fisheries management.