Military expenditure analysis has emerged as a critical component of economic and geopolitical intelligence in the modern era. This paper presents CIPHER Intelligence, a comprehensive AI-powered platform for analyzing and predicting global military spending patterns across 211 countries spanning 54 years (1970-2024). We employ advanced machine learning techniques, particularly Random Forest regression models, to achieve 99.5% prediction accuracy for military expenditure forecasting based on economic indicators. The platform integrates data from multiple authoritative sources including SIPRI, World Bank, and IMF databases, processing over 10,761 country- year observations. Our methodology encompasses comprehensive data preprocessing, feature engineering, and multi-dimensional analysis including regional, temporal, and economic development perspectives. The system achieves remarkable predictive per- formance with R2 scores of 0.995 for absolute spending, 0.832 for GDP percentage, and 0.980 for per capita expenditure. Key findings reveal strong correlations between GDP size and military spending (r = 0.94), significant regional disparities in defense burden, and accelerating global military expenditure reaching $2.4 trillion in 2024. The platform’s web-based interface provides real-time predictions, interactive visualizations, and comprehensive analytical dashboards for researchers, policymakers, and defense analysts. This work demonstrates the effectiveness of machine learning in economic intelligence analysis and establishes a robust framework for predictive defense economics.