This paper develops a Predefined-Time Convergent and Noise-Tolerant Fractional-Order Zeroing Neural Network (PTC-NT-FOZNN) model, innovatively engineered to tackle Time-Variant Quadratic Programming (TVQP) challenges. The PTC-NT-FOZNN, stemming from a novel iteration within the variable-gain Zeroing Neural Network (ZNN) spectrum, known as FOZNNs, features diminishing gains over time and marries noise resistance with predefined-time convergence, making it ideal for energy-efficient robotic motion planning tasks. The PTC-NT-FOZNN enhances traditional ZNN models by incorporating a newly developed activation function that promotes optimal convergence irrespective of the model’s order. When evaluated against six established ZNNs, the PTC-NT-FOZNN, with parameter $0<\alpha ⩽1$, demonstrates enhanced positional precision and resilience to additive noises, making it exceptionally suitable for TVQP tasks. Thorough practical assessments, including simulations and experiments using a Flexiv Rizon robotic arm, confirm the PTC-NT-FOZNN’s capabilities in achieving precise tracking and high computational efficiency, thereby proving its effectiveness for robust kinematic control applications.