Asset pricing models play a critical role in finance, aiding investors, analysts, and policymakers in understanding the valuation and behavior of financial assets. The goal of this research is to further the field by investigating the creation of asset pricing models. By means of an extensive examination of extant literature and empirical data, this study aims to identify the principal elements impacting asset prices and to suggest improvements or novel frameworks for asset pricing. The research will commence with a comprehensive analysis of conventional asset pricing models, including the Capital Asset Pricing Model (CAPM) and its variations, emphasizing their advantages and disadvantages. Subsequently, it will delve into contemporary approaches including multifactor models, behavioral finance theories, and machine learning techniques, assessing their efficacy in capturing asset pricing dynamics. By synthesizing insights from theoretical and empirical research, this study seeks to identify the most relevant factors driving asset prices across different asset classes and market conditions. Moreover, it aims to provide practical implications for investors, asset managers, and policymakers, aiding them in making informed decisions regarding asset allocation, risk management, and policy formulation. Overall, By studying asset pricing mechanisms in greater detail, this research hopes to improve the accuracy and robustness of models used to value financial assets in volatile and dynamic markets.