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Accurately assessing pain remains challenging due to its inherently subjective nature, highlighting the need for normative and objective neural indicators to support pain diagnosis. Using large-scale electroencephalography (EEG) datasets (N = 936), the present study establishes normative values for laser-evoked brain responses (LEP) and develops a predictive model of perceived pain intensity. Three major findings emerged. First, we characterized the typical EEG features elicited by nociceptive laser stimuli and constructed their normative reference ranges using normative modeling. Second, within a linear mixed-effects model framework, subjective pain ratings were reliably predicted by LEP features, each of which demonstrated distinct predictive contributions. Third, multilevel parallel mediation analyses demonstrated that N1, N2, and P2 amplitudes, N2 latency, as well as the magnitude of γ-band event-related synchronization (γ-ERS) serve as key neural mediators linking stimulus intensity to subjective pain perception. Collectively, these results elucidate how different EEG features encode variability in pain perception and provide a normative neurophysiological foundation for future pain research and clinical applications.