Efficient and sustainable water management remains a critical challenge in organic agriculture, where input use is restricted and irrigation decisions must be carefully calibrated to avoid resource waste and crop stress. This study presents a practical and cost-effective solution combining Internet-of-Things (IoT) sensors with artificial intelligence (AI) algorithms to enhance the performance of drip irrigation systems in organic farming contexts. The proposed system integrates capacitive soil moisture sensors, temperature probes, and flow meters into a field-deployable network communicating via LoRaWAN. Sensor data, collected at 15-minute intervals, are transmitted to a cloud platform that also integrates localized weather forecasts and field-specific agronomic data, including soil characteristics and crop phenological stages. After data cleaning and noise reduction using Kalman filtering, the input stream is fed into a hybrid machine learning model combining Long Short-Term Memory (LSTM) neural networks and Random Forest regression. The model is retrained periodically to ensure robustness under dynamic field conditions. Based on the 48-hour irrigation forecasts, the system autonomously adjusts irrigation timing and duration through solenoid valve control, maintaining soil moisture within optimal ranges. The approach was field-tested on two organic vegetable farms (tomato and bell pepper) in southeastern Romania during the 2024 growing season. Compared to traditional irrigation scheduling, the system reduced total water use by 27% and increased crop yield by 15%, with a measurable improvement in water-use efficiency (from 5.8 to 7.1 kg/m³). These results validate the effectiveness of IoT- and AI-based systems for precision irrigation in small- to medium-scale organic farms. The solution demonstrates tangible benefits in resource conservation, productivity, and climate resilience, and offers a replicable model for enhancing decision-making in data-constrained agroecological systems.