During sustainable bioethanol production, Industry 4.0 (I4.0) technologies can address process complexity, raw material variability, and energy efficiency challenges, enhancing yield and sustainability. Despite these interests, the adaptation of I4.0 technologies in the bioethanol manufacturing industry is scarce and often limited to first- and second-generation bioethanol production. This chapter incorporates the application of I4.0 principles in process monitoring and control for bioethanol production processes, with relevant literature information. Real-time monitoring is critical for the optimization of bioethanol production, especially during pretreatment, enzymatic hydrolysis, fermentation, and distillation. The IoT-enabled sensors are helpful in real-time data acquisition of key process parameters, viz. temperature, pH, sugar concentration, and ethanol yield. Besides, sensors integrated with IoT systems enable predictive maintenance and anomaly detection during bioethanol production. The various AI and ML-based data analytics models, such as artificial neural networks (ANNs) and long short-term memory (LSTM) networks, can analyze sensor data and develop predictive models. These models can efficiently address process accuracy and computational efficiency in bioprocess control, including nonlinearities in bioethanol production. Advanced process control systems based on model predictive control (MPC) and fuzzy logic systems on the MATLAB-Simulink platform can efficiently optimize bioethanol production by adjusting process parameters in real-time. ML-based MPC solutions can offer faster computation than traditional ODE/PDE-based models, enabling real-time control in complex bioethanol production processes. Cyber-physical systems (CPS) integrate physical processes with digital control systems, creating a connected bioethanol production ecosystem for condition monitoring in smart factories. Digital twins can simulate bioethanol production processes, enabling virtual testing of control strategies. The application of cloud systems for processing large datasets from bioethanol plants enables efficient energy analysis and process optimization. Therefore, I4.0 technologies can efficiently enhance the sustainability of an advanced bioethanol production process (e.g., combining enzymatic hydrolysis with simultaneous saccharification and fermentation (SSF)) by reducing energy consumption and waste management.