MACHINE LEARNING-DRIVEN GOVERNANCE: PREDICTING THE EFFECTIVENESS OF INTERNATIONAL TRADE POLICIES THROUGH POLICY AND GOVERNANCE ANALYTICS

Since global trade is becoming more complicated, policymakers are relying on more advanced tools to predict, analyze, and decide. Thanks to machine learning (ML), predictive governance could change the way international trade policies are evaluated and shaped by adding more accuracy and speed. At present, it is estimated that 80% of major economies use data-driven analytics in some part of setting their trade policies. Meanwhile, the use of artificial intelligence in government is expected to reach $3.7 billion by the year 2027 with a growth rate of 34.5% annually. This study uses ML algorithms including time-series forecasting, regression analysis, and natural language processing in the analysis and prediction of the outcomes of international trade policies. The analysis draws from datasets covering over 70 countries and data on trading activities over 20 years to see the results of applying ML techniques to different trade policies. From the analysis, we can confirm that ML models improve policy outcome prediction accuracy by up to 25% more than using traditional econometric models. Besides, it becomes apparent from feature importance analysis that changes in the GDP, happenings at the world stage, and commodity prices influence and shape trade patterns. The statistics confirm how predictive governance is useful for tracking recent developments, limiting the effects of risks, and helping design quick policy responses in international trade. Lastly, the paper suggests how to apply machine learning in government and international organizations by dealing with data quality, transparency, and considering ethical concerns.