The human skin, the body’s largest organ, serves as a critical barrier against environmental aggressors, maintaining homeostasis and protecting internal tissues. Accurate prediction of skin irritation is vital for dermatological health, cosmetic safety, and managing skin disorders. Traditional invasive methods for skin assessment (e.g., biopsies), although effective, raise ethical concerns, highlighting the need for noninvasive bioengineering techniques. Methods such as transepidermal water loss (TEWL), laser Doppler velocimetry (LDV), skin color/erythema analysis, and skin hydration evaluation have revolutionized dermatological assessments. Chronic irritant contact dermatitis (ICD), prevalent in high-risk occupations like hairdressing and healthcare, is challenging to manage, impacting quality of life and work productivity. Understanding factors contributing to irritancy is essential, with predictive irritancy testing offering a strategic approach, focusing on substance testing and population testing. Recent advancements in computational toxicology, leveraging machine learning, have enhanced the prediction of skin irritation potential, though in vivo tests remain the gold standard. Visual scoring is the central tool in evaluating skin irritancy, yet objective measures such as TEWL, LDV, and skin color metrics are important for detecting subliminal irritation. Field studies in high-risk occupations have identified key risk factors for hand dermatitis, including atopic dermatitis history, chemical exposure, and inadequate recovery time. Despite experimental studies, pre-exposure barrier function is not a reliable predictor in real-world settings for potential irritancy. Multiple repeated exposure models and noninvasive bioengineering methods can better identify at-risk individuals. This review provides a comprehensive framework for integrating noninvasive techniques into dermatological practice, aiming to enhance skin irritation prediction and management in both research and clinical settings.