Cognitive load measurement using biological signals such as electroencephalography (EEG) and electrocardiography (ECG) has emerged as a reliable approach in neuroscience, human–computer interaction, and cognitive research. EEG captures brainwave activity across different frequency bands (delta, theta, alpha, beta, and gamma), while ECG provides heart rate variability (HRV) and autonomic nervous system responses, both of which are strongly correlated with cognitive workload. Machine learning (ML) techniques, including Support Vector Machines (SVMs), Random Forest, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, have been applied to classify cognitive load levels based on extracted features such as power spectral density, coherence, event-related potentials, and R-R interval variability. Studies have reported classification accuracies exceeding 85% when using feature selection and deep learning models trained on EEG-ECG datasets from cognitive tasks like n-back tests and mental arithmetic. The results indicate that EEG features, particularly in the theta and alpha bands, along with HRV metrics from ECG, contribute significantly to distinguishing low, moderate, and high cognitive load states. These findings have practical applications in adaptive learning systems, neuro ergonomics, and mental workload assessment, enabling real-time monitoring and optimization of cognitive performance.