In the era of big data, economists are exploring new data sources and methodologies to improve economic forecasting. This study examines the potential of big data and machine learning in enhancing the predictive power of international macroeconomic forecasting models.The research utilizes both structured and unstructured data to forecast Korea's GDP growth rate. For structured data, around 200 macroeconomic and financial indicators from Korea and the U.S. were used with machine learning techniques (Random Forest, XGBoost, LSTM) and ensemble models. Results show that machine learning generally outperforms traditional econometric models, particularly for one-quarter-ahead forecasts, although performance varies by country and period.