Introduction: This study adopts underexploited global data from key institutions like the United Nations and International Monetary Fund to enhance practical applications of machine learning methods for economic and financial investigations. This study applies advanced sentiment analysis technologies to novel textual documents in order to clarify their influence on asset and energy market directions. This study leads with standard prediction methods in machine learning technologies that address present research questions and develop progressive knowledge on worldwide economic and financial systems.

Methodology: First, we cleaned and pre-processed the dataset. This study used current natural language processing algorithms to conduct sentiment analysis of textual documents from which interesting features were produced. The extracted results became part of three machine learning models that combined regression analysis with decision trees and neural networks. Multiple performance indicators and logical processing mechanisms guided the study to evaluate models while safeguarding both data confidentiality and model privacy.

Findings: The findings revealed that the sentiment data from IMF and UN textual content demonstrate substantial forecasting power for energy and commodity market movements, proving that ML techniques surpass previous prediction approaches. Neural networks surpassed the evaluated methods by achieving better accuracy to show their capacity to decode and prognosticate complicated market behaviours.

Conclusion: Empirical applications that cover alternative data sources allow financial experts to achieve an improved comprehension of global market functions. The system boosts decision-making capabilities through targeted analytical instruments that display optimal performance across various economic indicator datasets. This groundbreaking study extends beyond traditional financial analysis that depends on traditional banking records through examinations of nonstandard ML methods combined with noncommercial data resources. This research finds new machine learning applications across corporate finance, governance, and behavioural finance that fill existing study voids and broaden both economic and financial knowledge.