As global food security becomes increasingly critical due to population growth and climate change, greenhouses play a vital role in producing high-quality crops in controlled environments. This paper aims to develop and implement an advanced and comprehensive energy management platform based on the Quantum Particle Swarm Optimization (QPSO) algorithm that is capable of performing specialized monitoring and control services for smart greenhouses. The control platform comprises communication-assisted decision-making and control modules developed and tuned for heterogeneous power distribution systems that accommodate renewable energy generators, HVAC, energy storage device, pumps, water reservoir CO2 injector, dehumidifier, smart meters, and sensors. The main objective is to formulate and implement a dynamic nonlinear QPSO (NQPSO) framework embedded in a predictive model to optimally control the operation of the greenhouse and manage the power flow, ensuring highly efficient use of energy. The framework aims to ensure optimal conditions for crop growth by accurately regulating indoor microclimate variables while minimizing energy exchange with the grid during periods of low renewable energy generation, thus ensuring efficient operation. The predictive NQPSO framework is compared with classical optimization Particle Swarm Optimization (PSO) and Pyomo with Ipopt as a solver through extensive numerical simulations, demonstrating its superior performance and effectiveness.