This research develops a data-based system which reveals concealed market patterns through its identification of separate market regimes that produce unique return, volatility, and risk characteristics. The current financial models fail to recognize the intricate relationships which exist between different asset classes, including stocks and bonds, interest rates, commodities, and economic data indicators. The research employs a multivariate Hidden Markov Model (HMM) with improved data preprocessing techniques and principal component analysis (PCA) to process data from 2010 to 2025. The developed system detects nine separate market states which match actual economic and financial market situations. The market expansion phases produce strong investment returns at 20-30% annual rates while keeping market volatility at 12% but the contractionary phases lead to dangerous market conditions and negative investment results. The market transitions between different states occur at a slow pace because market conditions tend to stay stable instead of experiencing abrupt changes. The model shows a 100% probability that the market operates under Regime 8 which produces stable returns with a 1.6 Sharpe ratio during November 2025 while showing limited market volatility. Overall, the results highlight the cyclical yet persistent nature of market behavior and provide practical tools for improving risk management, policy assessment, and data-informed investment decisions.