Accurate demand forecasting of culinary ingredients is critical for ensuring supply chain efficiency, minimizing waste, and maintaining economic stability within food industries. In emerging markets such as Thailand, the volatility of food consumption patterns—driven by seasonality, cultural preferences, and economic fluctuations—poses significant challenges for traditional forecasting models. This study proposes an integrated analytical framework combining temporal modeling techniques and artificial intelligence-based approaches to predict culinary ingredient consumption patterns in Thai markets. The research synthesizes time series methodologies, including ARIMA and SARIMA, with machine learning models such as artificial neural networks (ANN), hybrid ARIMA-ANN systems, and regression-based approaches to enhance predictive accuracy.

The proposed framework leverages historical consumption data, seasonal indicators, and external economic signals to construct a hybrid predictive model capable of capturing both linear and non-linear patterns. By integrating temporal analytics with AI-driven learning mechanisms, the model addresses limitations associated with standalone forecasting techniques, particularly their inability to handle complex demand dynamics. The study incorporates empirical insights from Thai food market datasets and evaluates model performance using comparative metrics such as accuracy, stability, and adaptability.

Findings indicate that hybrid models significantly outperform traditional statistical approaches, particularly in scenarios involving high variability and short shelf-life products. The integration of machine learning enhances the model’s ability to adapt to changing consumption trends, while time series components ensure robust handling of temporal dependencies. The study also identifies critical factors influencing model performance, including data quality, feature selection, and algorithm configuration.

This research contributes to the field by providing a comprehensive, scalable, and data-driven forecasting framework tailored to food industry applications. It offers practical implications for supply chain optimization, inventory management, and policy planning in Thailand and similar markets. Furthermore, the study highlights future research directions, including real-time forecasting systems and the incorporation of deep learning techniques for enhanced predictive capabilities.