This empirically investigates the long-term trend and predictability of total advertising expenditure in Korea under changing macroeconomic conditions. Using annual data on total advertising expenditure and nominal GDP from 1985 to 2022, the study reexamines the Principle of Relative Constancy (PRC) and systematically compares the performance of traditional time series models with machine learning and deep learning-based forecasting models. To test relative constancy, the Two One-Sided Test (TOST) procedure and linear trend analysis were conducted. The results indicate that the advertising expenditure-to-GDP ratio has generally remained within the range of 0.6%-1.1% over the long term. However, equivalence was rejected under a ±0.1% tolerance margin, and the year coefficient showed a statistically significant negative value, suggesting the existence of a gradual structural decline in the share of advertising expenditure. In the forecasting performance comparison, the Bayesian Vector Autoregression (BVAR) model achieved the lowest error among the time series models, while the Long Short-Term Memory (LSTM) model recorded the lowest RMSE among all models, demonstrating the strongest predictive performance. Furthermore, five-year-ahead forecasts suggest that the post-COVID-19 decline in the advertising expenditure share is likely to remain relatively stable at a level below 1%. By reassessing the statistical validity of the PRC hypothesis and empirically comparing the relative effectiveness of different forecasting methodologies, this study provides an analytical framework that can contribute to mid- and long-term strategic planning and policy decision-making in the advertising industry.