The implementation of efficient pricing strategies is highly important for business growth in this competitive market. Today's market environments are volatile, uncertain, and ambiguous every corner. Traditional pricing models are not able to capture all these interactions. In this research, we developed a comprehensive framework integrating Bayesian into game theory settings for price prediction. In Bayesian demand models, new data are used for reevaluating old conclusions and previous predictions, while competitor game theoretic models examine the response emanating from competitive reaction based on Nash equilibrium concepts. In practice, this combined method is helpful for setting prices that balance profits and market share, adapt to real-time market conditions, and predict the competitive action. The theoretical implications of the study include pricing decision-making under uncertainty and the practical implications are real-time pricing changes in competitive landscapes. Future research can be done to validate the results empirically across a variety of industries with an aim to optimize the performance of the framework in real practice.