Trade value prediction (TVP) is major for understanding financial dynamics and directing policy decisions in the perspective of complex systems science. The study emphases on an analytical model intended to predict future trade values by evaluating financial indicators, past trade data, and geopolitical powers. By using advanced statistical models and machine learning techniques, the model explores relationships and patterns in trade flows among countries. The perceptions increased from this technique offer beneficial support for policymakers and businesses, guiding them to forecast the effects of financial and policy changes on global trade. Also, the study emphasizes the importance of a complicated method to enhance the accuracy of trade predictions and aid tactical decision-making in a worldwide interconnected economy. This study proposes trade value prediction using hybrid graph convolutional recurrent neural network with Lion optimizer algorithm (TVP-HGCRNNLOA) methodology. The objective function of the TVP-HGCRNNLOA methodology is to develop an accurate predictive model for trade values between countries. Primarily, the TVP-HGCRNNLOA approach undergoes the data normalization by employing linear scaling normalization technique. Then, the hybrid graph convolutional recurrent neural network (HGCRNN) method is used for forecasting process. At last, the TVP-HGCRNNLOA model performs the hyperparameter tuning by utilizing the Lion optimization algorithm model. The experimental analysis of the TVP-HGCRNNLOA methodology is investigated in terms of various measures under mean squared error, mean absolute error, and mean absolute percentage error. The performance validation portrayed the superior performance of the TVP-HGCRNNLOA methodology over other existing approaches.