Financial models are essential tools in shaping investment strategies, managing risks, and informing economic policies. With the growing complexity of global markets, evaluating the effectiveness, risk exposure, and sustainability of various financial models is crucial for investors, regulators, and policymakers. Traditional methodologies, such as discounted cash flow (DCF), the capital asset pricing model (CAPM), and modern portfolio theory (MPT), have long been the foundation of financial decision-making. However, the emergence of machine learning algorithms, algorithmic trading systems, and decentralized finance (DeFi) platforms has introduced innovative models that challenge conventional financial frameworks. This study conducts a comparative assessment of both established and modern financial models, focusing on their efficiency in resource allocation, resilience to market fluctuations, and long-term viability. It examines key factors such as predictive performance, volatility management, and responsiveness to economic disruptions. Additionally, the paper explores how AI-powered financial models enhance real-time risk evaluation and strategic planning while addressing concerns surrounding transparency, model reliability, and regulatory compliance. The inclusion of environmental, social, and governance (ESG) considerations in financial modeling further refines the analysis, emphasizing the broader economic and ethical implications of financial decision-making. Through an in-depth review of historical trends and industry case studies, this research highlights the strengths and limitations of various financial models. The findings underscore the need for a dynamic approach that integrates classical financial theories with technological advancements and sustainable investment principles to build more adaptive, responsible, and resilient financial systems.