Enhancing precision agriculture with AI has the potential of providing better results on crop yield expectations, especially for efficient provision of food and optimal resource utilization. Ordinary machine learning algorithms are quite stable but cannot work well in new real-time conditions. In contrast, reinforcement learning techniques are very flexible, as they work with a feedback loop from the environment in the process of parameter tuning. The purpose of this research is to understand how RL can be applied to develop crop yield-maximizing models and factors such as climate data over the years, current soil composition, and practices that are used in the management of the crops. This study utilizes the ‘Synthetic Agricultural Yield Prediction Dataset’ from Kaggle, containing 20,000 samples. Moreover, Recurrent Neural Networks (RNNs) are used for capturing temporal details of data for yield prediction with an R2 value of 0.93. In this research, two RL methods are experimented for yield maximization, namely Proximal Policy Optimization (PPO) and Q-Learning. It is shown that PPO is more efficient than Deep Q-Learning (DQN) with the percentage increase of 16.26% compared to DQN. This work integrates RNN for yield prediction and reinforcement learning (PPO, DQN) for decision-making, providing a scalable framework for precision agriculture.