The rarity and heterogeneity of liposarcomas (LPS) pose significant challenges in their diagnosis and management. In this work, a series of metal–organic frameworks (MOFs) engineering is designed and implemented. Through comprehensive characterization and performance evaluations, such as stability, thermal-driven desorption efficiency, as well as energy- and charge-transfer capacity, the engineering of group IV bimetallic MOFs emerges as particularly noteworthy. This is especially true for their derivative products, which exhibit superior performance across a range of laser desorption/ionization mass spectrometry (LDI MS) performance tests, including those involving practical sample assessments. The top-performing product is utilized to enable high-throughput recording of LPS metabolic fingerprints (PMFs) within seconds using LDI MS. With machine learning on PMFs, both the LPSrecognizer and LPSclassifier are developed, achieving accurate recognition and classification of LPS with area under the curves (AUCs) of 0.900–1.000. Simplified versions are also developed of the LPSrecognizer and LPSclassifier by screening metabolic biomarker panels, achieving considerable predictive performance, and conducting basic pathway exploration. The work highlights the MOFs engineering for the matrix design and their potential application in developing metabolic analysis and screening tools for rare diseases in clinical settings.