Here, we describe a neural network for isotopic cluster charge assignment using a unique mass defect encoding method. The performance of the new workflow, IsoDec, was compared on protein fragmentation spectra from different instruments and on large top-down proteomics LC-MS/MS files. Overall, IsoDec produced similar sensitivity to existing tools but with improved accuracy and speed. TopFD tended to have a higher number of clusters assigned, but a smaller percentage of those clusters matched protein fragments. In contrast, FLASHDeconv tended to have a higher percentage of clusters matched but at the cost of assigning fewer total clusters. Importantly, the improvement in performance with IsoDec is due to the neural network strategy for charge assignment, not simply stricter filtering of assigned clusters. Overall, IsoDec presents a novel strategy for encoding raw mass spectrometry data for AI applications and reveals the potential for lightweight and specialized AI to help automate analysis of challenging data.