Given the growing attention to food functionality for its potential in disease prevention and health promotion, evaluating the multifunctional health effects of foods is essential for advancing functional food science. However, conventional single-function assays are time-consuming and inefficient for comprehensive assessment. This chapter presents a conceptual framework for the simultaneous estimation of multiple food functionalities using machine learning, specifically artificial neural network (ANN)-based models. The system utilizes intracellular protein expression profiles obtained from cultured human cells treated with food extracts, enabling functionality estimation without requiring compound-level data. Each ANN is optimized for a specific functionality, allowing parallel prediction of multiple biological activities. To enhance robustness and generalizability, synthetic training data were generated through noise-based augmentation and model performance was evaluated using external test datasets. While estimation accuracy may be slightly lower than compound-based models due to inherent cellular variability, the system fulfills the requirements for preliminary screening. This strategy offers a promising solution for multiparametric functional food evaluation, particularly when compound-level information is unavailable. Moreover, we discuss future directions involving the integration of big data resources and network pharmacology to develop a more comprehensive system capable of predicting a wider range of functional properties in complex food matrices.