This study investigates the application of Principal Component Analysis (PCA) in simplifying multivariate financial data for portfolio risk analysis. The research aims to assess the effectiveness of PCA in reducing dimensionality, enhancing the accuracy of risk assessment models, and optimizing investment strategies for risk-adjusted returns. A quantitative methodology was employed, using historical financial datasets from 2020 to 2024, standardized preprocessing, and PCA extraction of principal components. The first three principal components accounted for 75.2% of the variance, confirming their significance in capturing portfolio risk. Regression analysis revealed an improvement in model accuracy from an adjusted R2 of 0.62 to 0.88, while portfolio risk exposure was reduced by 3.4% through PCA-based asset selection. The correlation between PCA-extracted factors and portfolio performance increased from 0.82 in 2020 to 0.88 in 2024, underscoring PCA’s growing predictive alignment with market trends. The study concludes that PCA enhances financial decision-making by isolating key risk drivers, improving model precision, and informing diversification strategies. It recommends integrating PCA with machine learning techniques, updating models with real-time data, and optimizing computational performance for high-frequency financial environments.