This work presents a comprehensive computational research compendium focused on AI-guided mRNA therapeutic design. The study integrates artificial intelligence, bioinformatics, and systems biology to develop in-silico frameworks for mRNA sequence optimization, molecular validation, and network-level modeling across multiple biomedical domains.The compendium introduces structured pipelines for mRNA design and evaluation, including codon optimization, RNA secondary-structure analysis, translational efficiency modeling, immunogenicity risk reduction, and multi-parameter validation scoring. These frameworks are applied to diverse theoretical case studies, including neurodegenerative disorders, autoimmune tolerance induction, rare genetic diseases, and harm-reduction strategies, demonstrating the adaptability of the computational methodology rather than clinical efficacy.All analyses are conducted entirely in silico and are intended for research, educational, and hypothesis-generation purposes only. No laboratory experiments, biological materials, human subjects, or animal models are involved. The work does not diagnose, treat, cure, or prevent any disease and requires extensive experimental validation before any real-world application.By emphasizing reproducible computational methods, transparent validation metrics, and open-science principles, this compendium serves as a foundational reference for researchers exploring AI-driven approaches to mRNA therapeutic design and systems-level biomedical modeling.