Forest phenology, which examines the seasonal cycles of forest ecosystems, including leaf emergence, flowering, and senescence, is a critical tool for assessing ecosystem health and understanding the impacts of climate change. Conventional monitoring methods frequently show important deficiencies in temporal resolution and automation capabilities, substantially obstructing our capacity to track evolving ecological changes. This study aimed to create and confirm an automated deep learning method for studying how forest characteristics change over time using digital photos taken frequently in the Gir Deciduous Forest Ecosystem, India. We created a system combining time series analysis and phenological modeling to extract and predict seasonal plant growth patterns from PhenoCam imagery, concentrating on Green Chromatic Coordinate (GCC) values. The performance evaluation showed extraordinary accuracy, with an overall Root Mean Squared Error (RMSE) of 5.80 days, an R-squared value of 0.995, and a Mean Absolute Error (MAE) of 5.00 ± 2.94 days. Leave-one-out cross-validation (LOOCV) provided substantially stronger confirmation of the model’s robustness, consequent in a root mean squared error of 5.00 ± 2.94 days and a mean absolute error of 5.00 ± 2.94 days. Gradual changes during monsoon-driven green-up periods were effectively caught by the system, along with rapid transitions during dry seasons, characteristic of this semi-arid forest ecosystem. Our study demonstrates the potential of combining digital repeat photography with deep learning to improve phenological monitoring in protected forest regions, offering crucial information for conservation and forest management in the context of evolving climatic patterns.