Reinforcement Learning (RL) has emerged as a powerful paradigm for automating enterprise planning, offering capabilities for sequential decision-making, dynamic optimization, and adaptive strategy formulation. Unlike supervised or unsupervised learning, RL models interact with environments, learn policies through feedback, and optimize long-term rewards—making them especially suitable for complex business systems such as supply chains, logistics, finance, energy management, workforce scheduling, and strategic planning. This paper presents the theoretical foundations of RL in enterprise planning by integrating classical RL theory, Markov Decision Processes (MDPs), Bellman equations, dynamic programming, temporal difference learning, and multi-agent RL. A Reinforcement-Learning-Based Enterprise Planning Framework (RL-EPF) is proposed along with conceptual diagrams, agent–environment interaction models, and architectural workflows. The study concludes with organizational implications, limitations, and future research directions.