Accurate streamflow forecasting is critical for mitigating the impacts of hydrological extremes and guiding sustainable water resource management, particularly in poorly gauged tropical catchments. This study presents a hybrid forecasting framework that integrates Neural Network Seasonal Autoregressive Integrated Moving Average using exogenous variables (NN-SARIMAX) models with nonlinear principal components (NLPCs) derived from CHIRPS precipitation data, and large-scale ocean–atmosphere indices (macroclimatic variables, MVs). Four monthly models were developed and tested for the Tocaría River basin in the Colombian Orinoquía region: (1) a baseline SARIMA (4,0,4) (0,0,3)₁₂ model; (2) SARIMAX with exogenous MVs; (3) NN-SARIMAX with NLPCs; and (4) a hybrid NN-SARIMAX combining both MVs and NLPCs. The hybrid model achieved the best performance with an R² of 0.78 during the validation period. These results underscore the effectiveness of integrating local precipitation variability and large-scale climatic drivers to enhance forecast accuracy under data-scarce conditions. The proposed methodology offers a transferable approach for operational forecasting in ungauged or sparsely monitored basins, contributing to early warning systems, drought preparedness, and adaptive water governance in vulnerable tropical regions.