This paper proposes a comparative study of two of the most effective approaches to stock market forecasting: the LSTM network and the RL model. The proposed AI model Long Short Term Memory which is a type of recurrent neural network is efficient in handling long dependencies within the sequential data, a characteristic which makes it plausible in the exercise of predicting trends by feeding it historical stock data. Reinforcement Learning, however, treats stock trading as a decision-making process, and extracting the best trading strategy based on rewards from the respective market actions. In this research, we compare each model’s predictive ability, its ability to capture frequent fluctuations, and its computational performance in terms of standard financial metrics. That is why the experimental results have established that LSTM models are suitable for stable conditions in the market where there are evident trends. RL models also show remarkable versatility in terms of reacting to fluctuations in price since strategies are modified on the fly while traditional models are rigid and fixed thus providing a powerful edge in real-time trading environments. Based on the findings of this study, considerations for developing integrated models that combine LSTM’s trend-analysis feature and RL’s decision-making advantage to enhance stock market prediction precision and reaction capability are discussed hereunder.