The administration of sedation in Intensive Care Units (ICU) is a vital and complex task, essential for the well-being of critically ill patients. Proper dosing is crucial, as both under-dosing and overdosing have adverse consequences for patients, including risks such as accidental extubation, ventilator disconnections, delirium, prolonged ventilation, ICU stays, and even increased mortality. Monitoring sedation levels is essential to maintain a balance in sedative administration. Clinical scales (RASS/SAS) are currently used as the reference method, but they have limitations. These scales may be inadequate when the patient is unresponsive or when neuromuscular blockers are used. An alternative is electroencephalography (EEG)-based monitors, though they also have limitations in accurately representing the level of hypnosis. The proposal is to develop an advanced multiparameter system called ROMANTIC, which utilizes machine learning algorithms to monitor sedation status continuously. The aim is for this system to surpass current techniques and provide a more accurate determination of sedation levels. ROMANTIC would incorporate a variety of variables, demographic, pharmacological, hemodynamic, respiratory, and EEG data, to predict sedation status in three categories: under-dosing, appropriate dosing, and overdosing. With ROMANTIC, clinical staff (users) are expected to be able to determine sedation levels more quickly and accurately, reducing patients' wake-up times and possibly decreasing the incidence of delirium. In the long term, this could result in fewer ICU bed days, less time on mechanical ventilation, cost savings, and reduced complications. At the end of the project, the goal is to have a prototype model or software that allows non-specialized staff (users) to quickly determine sedation levels in the ICU. This machine learning-based model targets hospitals with ICUs and mechanical ventilation to offer more efficient and cost-effective clinical care. ROMANTIC seeks to innovate in sedation monitoring, providing a more advanced and precise tool for the care of critically ill patients (beneficiaries) in the ICU.