A growing body of research is focusing on the role of the active nature of neuronal membranes and cytoskeletal structures in the emergence of the action potential. Despite the success of long-standing electrophysiological models, such as the Hodgkin-Huxley model (HH) and Cable Theory (CT), these models ignore the multiphysics nature of neurons, i.e., the coupling between mechanical, electrophysiological, and biochemical properties. Furthermore, these models are predominantly phenomenological in nature and cannot straightforwardly be extended to account for the energetic aspects of the action potential. Altered variants of these models address some of these limitations and have incorporated mechanical considerations; although these models are often confined within a specific scope and lack a unified treatment of the multiphysics of the neuron. In contrast, the Poisson–Nernst–Planck (PNP) model has been shown to produce results consistent with CT models, while providing a more intuitive physics-based framework to account for multiphysics interactions. However, current implementations still rely on phenomenological HH conductance models for ion transport across membranes, limiting their wider applicability. Here, we apply instead the Onsager variational principle to neuronal membranes, deriving a thermodynamically consistent model that can capture the coupled non-equilibrium dynamics of ionic concentrations, membrane potential, and mechanical properties. This approach is shown to reproduce the PNP equation and the Nernst potential for the electrochemical gradient across the membrane. We also provide a novel derivation of stochastic ion channel gating dynamics and show that the proposed model captures the influence of membrane mechanics on ionic activity/membrane potential and vice versa. Although validated here for a 1D problem, the approach can be modified to include mechanical interactions, 3D neuronal geometries, and neuromodulation considerations. Overall, this work lays the foundation for further integration of multiphysics modelling into neuroscience, offering insights into the role of multiphysics in neuronal function and advancing our understanding of the efficacy of current neuromodulation techniques.