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PURPOSE To enable acceleration in 3D multi-echo gradient echo (mGRE) acquisition for myelin water imaging (MWI) by combining joint parallel imaging (JPI) and joint deep learning (JDL). METHODS We implemented a multi-step reconstruction process using both advanced parallel imaging and deep learning network which can utilize joint spatio-temporal components between the multi-echo images to further accelerate 3D mGRE acquisition for MWI. In the first step, JPI was performed to estimate missing k-space lines. Next, JDL was implemented to reduce residual artifacts and produce high-fidelity reconstruction by using variable splitting optimization consisting of spatio-temporal denoiser block, data consistency block, and weighted average block. The proposed method was evaluated for MWI with 2D Cartesian uniform under-sampling for each echo, enabling scan times of up to approximately 2 minutes for 2 mm × 2 mm × 2 mm $2{\rm{mm}} \times 2{\rm{mm}} \times 2{\rm{mm}}$ 3D coverage. RESULTS The proposed method showed acceptable MWI quality with improved quantitative values compared to both JPI methods and JDL method individually. The improved performance of the proposed method was demonstrated by the low normalized mean square error and high-frequency error norm values of the reconstruction with high similarity to the fully-sampled MWI. CONCLUSION Joint spatio-temporal reconstruction approach by combining JPI and JDL can achieve high acceleration factors for 3D mGRE based MWI. This article is protected by copyright. All rights reserved.