In the context of modern industrial environments characterized by heightened automation and digital transformation, Network Operation Center (NOC) operators face a significant escalation in task complexity and cognitive workload (CWL). As human-centered digitalization and machine learning technologies advance, the role of human operators increasingly involves continuous monitoring, real-time decision-making, and interaction with complex systems—demanding elevated levels of cognitive engagement. CWL represents the mental state arising from the interaction between task demands and the cognitive resources available to the individual. Traditional CWL assessment methods, predominantly reliant on subjective tools such as self-report questionnaires and structured interviews, offer limited reliability and intersubjective comparability. Consequently, there is a growing interest in objective, neurophysiological approaches—including Galvanic Skin Response (GSR), electroencephalography (EEG), pupillometry, and electrocardiography (ECG)—which provide more direct and quantifiable insights into mental workload. This study critically examines various methodologies for measuring cognitive workload in NOC settings, evaluating their applicability, precision, and practicality. By analyzing both subjective and objective measurement techniques, the research aims to identify robust approaches that can enhance situational awareness and performance monitoring, ultimately contributing to improved operational efficiency and operator well-being in high-demand digital environments.