This study presents a cost-effective and scalable automated greenhouse control system designed to optimize crop-growing conditions and enhance resource efficiency. The system integrates an ATmega2560 microcontroller with environmental sensors—DHT11, soil moisture, and LDR—to continuously monitor temperature, humidity, soil moisture, and light intensity. Real-time sensor feedback drives actuators, including DC fans, water pumps, and LED grow lights, to maintain optimal environmental parameters (26–30 °C temperature, 67–70% humidity, 69–71% soil moisture) without manual intervention. A key innovation lies in the multi-parameter control architecture that simultaneously regulates multiple variables, avoiding conflicts commonly encountered in sequential control systems. Experimental results demonstrate a 35% reduction in energy consumption through intelligent actuator scheduling, 50% water savings via moisture-based irrigation, and 60% cost savings using low-cost modular components. The system significantly improves environmental stability, reducing temperature fluctuations to ±1.5 °C compared to ±3.5 °C in conventional setups, and maintains consistent soil moisture to minimize water waste. The design supports scalability, with potential for future upgrades, including CO₂ monitoring and solar power integration, which enables off-grid operation. Moreover, the system enables year-round cultivation of seasonal crops by precisely managing microclimate conditions. Its affordability and adaptability make it particularly suitable for small-to-medium farms in developing regions. By combining precision agriculture techniques with accessible technology, the proposed solution effectively bridges the gap between advanced greenhouse automation and real-world applicability. Future work will explore predictive algorithms for anticipatory control and enhanced integration of renewable energy sources to further improve sustainability and performance.