In industrial environments characterised by harsh communication conditions and spectrum scarcity, cognitive radio networks (CRNs) offer promising solutions to enhance communication reliability and efficiency. However, leveraging CRNs in such environments requires addressing challenges related to spectrum detection, allocation, and energy-efficient transmission. To tackle these challenges, we propose an integrated system that combines intelligent spectrum detection, spectrum allocation using reinforcement learning, Dynamic spectrum access (DSA) with Proximal policy optimisation (PPO), and energy-aware transmission optimisation. Our proposed system aims to enhance the performance and sustainability of CRNs in industrial settings by accurately identifying available spectrum bands, optimising spectrum allocation, and dynamically adjusting transmission parameters based on real-time channel conditions and energy constraints. We employ spectral density estimation and detection thresholds for intelligent spectrum detection, reinforcement learning-based spectrum allocation for efficient spectrum utilisation, and DSA with PPO for adaptive transmission parameter adjustment. Additionally, energy-aware transmission optimisation ensures minimal energy consumption while maintaining reliable communication links. Experimental results demonstrate the effectiveness of our proposed system in optimising energy consumption, improving data transmission efficiency, and enhancing network reliability in industrial CRNs. Overall, our system presents a comprehensive approach to address the challenges of spectrum scarcity and energy efficiency, making it suitable for various industrial communication applications.