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This paper introduces an advanced framework for accelerated processing of diffusion‐weighted imaging (DWI) data that utilizes an entire‐image modeling approach to optimize the estimation of diffusion parameters from DWIs by mapping input diffusion data to predicted signals and estimating parameter values via a stochastic gradient descent optimizer (Adam). To validate this approach, we applied this framework to diffusion basis spectrum imaging (DBSI) and analyzed in vivo human brain and ex vivo mouse brain DWIs. Results demonstrate significant improvements to computational speed and signal‐to‐noise ratio (SNR) in estimated parameter maps compared to standard DBSI. Our approach is applicable to any diffusion signal representation and enables rapid and reliable signal partitioning in complex microstructural environments, demonstrating the potential of this framework for future neuroimaging research. We introduce a new framework for accelerated processing of diffusion‐weighted imaging (DWI) data using a machine learning approach to optimize parameter estimation. We demonstrate that this new method, called DBSIpy, significantly improves computational speed and robustness to Rician noise compared to the standard DBSI method, with the improvements being generalizable to other DWI signal representations.