As the market competition rapidly increases, companies are required to analyze their market strategically and advanced simulation techniques are needed to assist them in effectively achieving that. Often, the interactions between consumers and firms along with other economical components are complex and challenging to interrelate through modeling. Therefore, this research builds an agent-based simulation framework which is capable of modeling competition within a market as well as the behaviors of firms along with the decisions made by consumers. An integrated approach is taken with adaptive processes along with game theory and reinforcement learning to enact the changing competitive nature of the markets. The provided framework allows for the integration of technological improvements and provides the ability to accurately provide for the competent behavior of the agents. The aggregate results of using different degree of competition, variable pricing and fluctuation in demand enabled us to analyze the best market outcomes using controlled experimentation. The study also assists in helping the industry formulate the worst and best strategies along with providing deep understanding of the impacts of market shocks. Additionally, the agents are measured in regard to their behaviors, market equilibrium, and strategic responses through a mathematical model along with having the real market data benchmarked against to ensure accuracy and practicality. This aide modern research in utilizing an agent-based approach to business strategy and shifts the focus to reproving challenges with scalability along with providing more explanations to AI powered marketing simulators. Integrating AI-assisted predictive analytics and AI multi-agent learning are proposed as future extensions for more refined market forecasting and strategic planning.