Deuterium（D）and tritium（T）have been regarded as the first-generation fuels for achieving commercial fusion energy. However，the utilization of the radionuclide tritium introduces concerns related to radioactive safety. This study sought to investigate methods for estimating airborne tritium sources following a fusion reactor incident. An algorithm that combines an adaptive Kalman filter with a deep feedforward neural network was developed to determine the tritium release height and rate. By utilizing observed data both pre- and post-filtering as inputs，the neural network's predictions for the tritium release rate were analyzed. The findings indicate that filtering significantly lowers the prediction errors. Considering a 20% monitoring error，the average relative error for the estimated release height is approximately 3% and that for the release rate is approximately 4%.