Rheumatoid Arthritis (RA) is a complex autoimmune disease requiring sophisticated modelling approaches for accurate progression prediction and treatment optimisation. This study introduces a novel application of Physics-Informed Neural Networks (PINNs) that integrates established pathophysiological laws with data-driven learning to predict three critical RA biomarkers: C-reactive protein (CRP), Disease Activity Score 28 (DAS28), and lymphocyte count. Our approach embeds ordinary differential equations representing inflammatory dynamics, disease activity, and immunological response directly into the neural network’s loss function. Validation on a synthetic dataset of 400 patients over 12 months demonstrates superior performance with a mean \(R^2\) of 0.7053, representing a 34.5% improvement over linear regression baseline. The model successfully learned 12 interpretable physical parameters, revealing unexpected medical insights including moderate CRP-DAS28 coupling ( \(\gamma \) = 0.1075) and significant endogenous self-resolution capacity ( \(\eta \) = 0.0939). Clinical correlation analysis confirmed preservation of known medical relationships while respecting physiological ranges. This work establishes PINNs as a powerful tool for chronic disease modelling, offering enhanced interpretability, improved generalisation, and the potential for discovering novel pathophysiological insights with direct clinical applications.