Messenger RNA (mRNA) has rapidly advanced from a platform for prophylactic vaccination to a credible modality for cancer immunotherapy. Yet its successful translation to oncology is constrained by tumor heterogeneity, immune suppression, and the pharmacology of mRNA-lipid nanoparticle (LNP) systems, as well as modest in vitro-to-in vivo translatability during preclinical development. This chapter synthesizes a computationally driven framework for end-to-end design of mRNA cancer immunotherapies that couples multi-omics with modern machine learning and mechanistic modeling. We first outline the biological determinants of antigen processing and presentation across MHC classes, emphasizing how tumor mutational burden, clonal architecture, and microenvironmental cues shape neoepitope visibility. We then survey integrated pipelines for neoantigen discovery that combine immunopeptidomics-trained predictors with RNA/DNA evidence and HLA context, using toolchains such as pVACtools as exemplars while noting their current limits. Next, we detail sequence-level engineering of therapeutic mRNAs, codon and UTR optimization, linker/junction control, trafficking motifs, and structure, expression trade-offs, highlighting emerging AI models that jointly optimize translation efficiency and structural stability. On the delivery side, we review data-driven LNP design, including graph- and transformer-based models for lipid/formulation search and multiscale molecular dynamics to interrogate self-assembly, internal architecture, and endosomal escape. Finally, we describe systems biology methods, gene-regulatory network modeling, agent-based simulations, and quantitative systems pharmacology (QSP), to connect molecular design choices to predicted cellular and tissue-level responses, optimizing dosing, scheduling, and combination strategies. Across these layers, we propose practical integration points that reduce candidate attrition, shorten iteration cycles, and improve physiological relevance.