This paper introduces a novel framework for analyzing the finite-time stability of fractional-order delayed Clifford-valued neural networks. Different from existing studies based on separation methods, this work introduces a novel non-separation approach to study finite-time stability for fractional orders \(\sigma \in (0,1)\) and \(\sigma \in (1,2)\) . Specifically, we establish new inequalities that enable direct analysis without separating the Clifford-valued neural networks into real-valued neural networks. First, by utilizing the properties of the signum function and the norm of Clifford numbers, a new inner product-based inequality is established for the fractional-order derivatives of Clifford-valued functions. In addition, we develop a more convenient and generalized form of the Henry–Gronwall integral inequality that simplifies the analysis of certain integral equations. By employing these newly developed inequalities with an iterative technique, two novel algebraic criteria are derived to ensure the finite-time stability of the equilibrium point of fractional-order delayed Clifford-valued neural networks for fractional orders \(\sigma \in (0,1)\) and \(\sigma \in (1,2)\) without any separation. Finally, we present three simulation examples to verify the validity of the obtained results, including an application of associative memory to demonstrate their effectiveness in accurately restoring original color image patterns.
