This article presents a deep-learning-enhanced high-throughput mass spectrometry (MS) framework called MEISTER for multiscale biochemical mapping of the brain. The framework integrates deep-learning-based signal reconstruction, multimodal registration, and data integration methods to enable high-resolution, three-dimensional (3D) molecular profiling of brain tissues and single cells. MEISTER accelerates high-mass-resolving MS by 15-fold, enabling the imaging of millions of pixels across the brain with single-cell resolution. It also allows the mapping of single-cell biochemical profiles onto tissue sections, enabling multiscale characterization of spatial–biochemical organization of the brain. The framework was validated using computational simulations and experimental data, demonstrating its ability to reconstruct high-resolution mass spectra and ion images from noisy short transients. MEISTER achieved 3D mapping of the rat brain with unprecedented large volume coverage, high spatial resolution (50-μm lateral and 16-μm sections), and high chemical content (>1,000 lipid features). It also profiled 13,566 single cells isolated from five rat brain regions and built cell-type-specific chemical dictionaries, which were then mapped to the tissue images to obtain spatially resolved cell type distributions across the brain. The study also demonstrated the potential of MEISTER as a general multiscale tissue biochemical characterization approach by applying it to another tissue type, rat pancreas, and to molecules beyond lipids, such as peptides. The framework enables the identification of region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. The results show that MEISTER can robustly uncover anatomically specific biochemical profiles for the entire tissue volume, with high accuracy in classifying brain regions based on lipid profiles. The study also highlights the importance of integrating single-cell and tissue data for understanding the spatial organization of cell subpopulations within a certain anatomical region. The framework enables the resolution of thousands of brain lipid features over millions of pixels across a 3D volume and large cell population, while substantially reducing the data collection time. The results suggest that the framework is adaptable to different types of molecules in addition to lipids and peptides, and other organ systems. The study provides a blueprint for future development of multiscale technologies for biochemical characterization of the brain.This article presents a deep-learning-enhanced high-throughput mass spectrometry (MS) framework called MEISTER for multiscale biochemical mapping of the brain. The framework integrates deep-learning-based signal reconstruction, multimodal registration, and data integration methods to enable high-resolution, three-dimensional (3D) molecular profiling of brain tissues and single cells. MEISTER accelerates high-mass-resolving MS by 15-fold, enabling the imaging of millions of pixels across the brain with single-cell resolution. It also allows the mapping of single-cell biochemical profiles onto tissue sections, enabling multiscale characterization of spatial–biochemical organization of the brain. The framework was validated using computational simulations and experimental data, demonstrating its ability to reconstruct high-resolution mass spectra and ion images from noisy short transients. MEISTER achieved 3D mapping of the rat brain with unprecedented large volume coverage, high spatial resolution (50-μm lateral and 16-μm sections), and high chemical content (>1,000 lipid features). It also profiled 13,566 single cells isolated from five rat brain regions and built cell-type-specific chemical dictionaries, which were then mapped to the tissue images to obtain spatially resolved cell type distributions across the brain. The study also demonstrated the potential of MEISTER as a general multiscale tissue biochemical characterization approach by applying it to another tissue type, rat pancreas, and to molecules beyond lipids, such as peptides. The framework enables the identification of region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. The results show that MEISTER can robustly uncover anatomically specific biochemical profiles for the entire tissue volume, with high accuracy in classifying brain regions based on lipid profiles. The study also highlights the importance of integrating single-cell and tissue data for understanding the spatial organization of cell subpopulations within a certain anatomical region. The framework enables the resolution of thousands of brain lipid features over millions of pixels across a 3D volume and large cell population, while substantially reducing the data collection time. The results suggest that the framework is adaptable to different types of molecules in addition to lipids and peptides, and other organ systems. The study provides a blueprint for future development of multiscale technologies for biochemical characterization of the brain.