The human tongue perceives food through the synergistic sensation of both chemical (taste) and physical (temperature, texture, softness) cues. Inspired by such functionalities, electronic tongues (e-tongues) have been developed for applications ranging from food analysis to biomedical sensing. However, most reported e-tongues primarily capture chemical tastes, overlooking critical physical attributes. In addition, current e-tongues typically rely on heterogeneous sensor outputs (e.g., current, capacitance, impedance), which complicates circuit design and signal processing and leads to high system power consumption. Here, we present a fully potentiometric, monolithically integrated multimodal e-tongue capable of simultaneously sensing chemical attributes (e.g., salinity and acidity) and physical attributes (e.g., temperature, texture, softness) of food. Importantly, all integrated sensors self-generate a unified output─potential difference (mV)─thereby eliminating the need for external power. Such a fully potentiometric sensor integration also greatly simplifies the signal acquisition circuitry and drastically reduces the system power consumption. Assisted by machine learning algorithms, the proposed fully potentiometric e-tongue achieves high accuracy in discrimination of both fruits and beverages, outperforming conventional single-modality e-tongues. This work demonstrates a practical route toward holistic gustatory sensing and offers new opportunities for the development of biomimetic intelligent systems that closely replicate natural taste perception.