Economic Levels Forecasting System By Evaluating with more Accuracy Using ML, DL and AI Systems

The rise of Artificial Intelligence (AI) as a formidable tool in economic predictions is set to change earlier methods with unprecedented possibilities in processing big data sets and finding subtle patterns. The paper discusses the state-of-the-art impact of deploying machine learning models in economic forecasting. We intend to verify the effective implementation of AI techniques in predicting economic trend models through robust analysis, comparing with other methods currently in use, and their impacts on the decision-making processes. The paper begins with a brief background on the need for economic forecasting in a plurality of fields, i.e., government policymaking, financial markets, and corporate strategy formulation. Traditional forecasting methods are much criticized, and it frequently discusses limited efficacy in capturing complex and nonlinear relationships dynamically adapted to the market conditions. Machine learning algorithms, on the other hand, promise very flexible data-driven techniques that would expose underlying insights very deeply in diverse sources, from financial indicators to consumer behavior and to macroeconomic variables. This will be our focus: To analyze performance across the machine learning model spectrum in economic forecasting tasks—ranging from GDP growth projections to stock market forecasting. This is through empirical analyses that capture the exactitude and robustness of AI-driven forecasts with rich datasets spanning multiples of those economic indicators and historical trends.