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Product Disassembly has become an area of active research as it sup- ports sustainable development by aiding effective end-of-life (EOL) stage strategies like reuse, re-manufacturing, recycling, etc. In this work, we propose a new approach, 3D-PDNet, that can utilize 3D data from CAD assembly models to generate a feasible disassembly sequence. Our approach uses Graph-based learning to process the graph representa- tion of CAD models. Currently, the available 3D CAD model datasets lack ground truth disassembly sequences. We propose and curate a new dataset, the 3D-PD dataset, which includes ground truth information about the disassembly sequence for 3D product models. We carry out evaluation and analysis of results to explain the efficacy of the proposed method. Our approach significantly outperforms the existing baseline. We develop an Autodesk Fusion 360 plug-in that generates disassembly sequence animation, allowing intuitive analysis of the disassembly plan.