Accurate bird sighting predictions enhance tourism planning, helping visitors optimize birdwatching in natural parks. This study focuses on the Natural Park of Las Lagunas de la Mata y Torrevieja (Alicante, Spain), where AI models were developed for bird sightings forecasts. The analysis showed that data granularity affects model performance: high-frequency datasets with noise are harder to predict due to the lack of clear seasonal patterns, whereas aggregated datasets with weekly or monthly intervals reveal more structured trends with reduced noise. In all scenarios, the hybrid CNNLSTM model consistently outperformed the other studied models. Additionally, a web augmenter was implemented to enhance the Wikipedia page for the park, offering interactive visualizations, including dynamic maps with daily bird sighting predictions, aiding tourists in planning their visits.