This research delves into the neuroscientific dimensions of deception detection, providing deeper insights into the cognitive and physiological processes associated with concealing, hiding, or deceiving. The study focuses on the use of the P300 ERP-based odd-ball paradigm to investigate cognitive aspects such as attention, memory, and decision-making in deception detection. The development and validation of a new hybrid paradigm, supported by a Machine-Learning model, demonstrate promising outcomes. The paper discusses the significance of Neuro-ERP methods, their cognitive processing implications, objective indicators, and cross-examination utility, along with ethical considerations and limitations. The study involved 10 subjects, employing AEP and VEP techniques for processing textual, auditory, and visual stimuli. The proposed hybrid paradigms, tested alongside traditional methods, are explored in detail, and its post-implementation effects are validated through canonical correlation analysis and a Machine-Learning model, providing evidence-backed insights. The results and implications are thoroughly discussed in the context of real-world applications.