Accurate prediction of the projectile launch point can quickly locate enemy threat sources, provide critical intelligence support, and optimize counterattack strategies, holding significant strategic importance in the military field. This study addresses the problem of predicting projectile launch points and proposes a deep learning model that combines temporal convolutional network (TCN), bidirectional gated recurrent unit (BiGRU), and attention mechanism. The model aims to improve ballistic trajectory prediction accuracy, especially in complex battlefield environments, by backwardly inferring enemy projectile launch points to support counterattack strategies. Firstly, based on the ballistic model, a detailed projectile trajectory dataset was constructed by solving the six-degree-of-freedom rigid body ballistic equation for different launch angles and initial velocities. Then, the proposed TCN-BiGRU-Attention model captures long-term dependencies in the trajectory data by introducing the TCN structure and optimizes information weighting using the attention mechanism to enhance prediction accuracy. In simulation validation, compared with models like BiGRU, bidirectional long short-term memory (BiLSTM), and their improved variants, the TCN-BiGRU-Attention model demonstrated significantly superior performance in launch point prediction accuracy, particularly in reducing errors in both range and cross-range directions. Through multiple sets of simulation tests, the results indicate that the TCN-BiGRU-Attention model can stably provide accurate launch point predictions at various launch heights. At sea level, the model’s range error is only 8.3 meters, and the cross-range error is minimal, effectively predicting and striking the enemy’s launch point. This study provides theoretical basis and technical support for the implementation of enemy launch point prediction in future battlefield scenarios.