OBJECTIVES: Primary knee osteoarthritis (KOA) is a heterogeneous disease with clinical and molecular contributors. Biofluids contain microRNAs and metabolites that can be measured by omic technologies. Multimodal deep learning is adept at uncovering complex relationships within multidomain data. We developed a novel multimodal deep learning framework for clustering of multiomic data from 3 subject-matched biofluids to identify distinct KOA endotypes and classify 1-year post-total knee arthroplasty (TKA) pain/function responses. METHODS: In 414 patients with KOA, subject-matched plasma, synovial fluid, and urine were analysed using microRNA sequencing or metabolomics. Integrating 4 high-dimensional datasets comprising metabolites from plasma and microRNAs from plasma, synovial fluid, or urine, a multimodal deep learning variational autoencoder architecture with K-means clustering was employed. Features influencing cluster assignment were identified and pathway analyses conducted. An integrative machine learning framework combining 4 molecular domains and a clinical domain was then used to classify Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain/function responses after TKA within each cluster. RESULTS: Multimodal deep learning-based clustering of subjects across 4 domains yielded 3 distinct patient clusters. Feature signatures comprising microRNAs and metabolites across biofluids included 30, 16, and 24 features associated with clusters 1 to 3, respectively. Pathway analyses revealed distinct pathways associated with each cluster. Integration of 4 multiomic domains along with clinical data improved response classification performance, surpassing individual domain classifications alone. CONCLUSIONS: We developed a multimodal deep learning-based clustering model capable of integrating complex multifluid, multiomic data to assist in uncovering biologically distinct patient endotypes and enhance outcome classifications to TKA surgery, which may aid in future precision medicine approaches.