Robotic-assisted total joint arthroplasty (RA-TJA) is projected to account for 70% of all arthroplasties by 2030, yet its economic value and operational efficiency have yet to be thoroughly synthesized. While early literature emphasized technical precision, evolving payment models and implementation costs have shifted focus toward cost-effectiveness and workflow integration. To evaluate the economic and institutional viability of RA-TJA by synthesizing available evidence on capital costs, perioperative expenses, learning curves, throughput, and long-term adoption trends. This review also considers market competition, global uptake, and the need for standardized outcomes reporting. A narrative literature review of published cost analyses, real-world efficiency studies, and policy-relevant frameworks was conducted. Literature addressing per-case cost variability, bundled payments, and implementation strategies was analyzed. Emerging economic models and global diffusion patterns were incorporated to contextualize long-term feasibility. Robotic platforms consistently incur higher upfront and perioperative costs than manual or navigated arthroplasty. However, in both high- and low-volume institutions, these costs may be offset by reduced complications, shorter hospital stays, and improved discharge metrics. Efficiency gains are amplified through procedural clustering and experienced teams. Adoption is accelerating globally, facilitated by leasing models and market competition, yet value remains highly dependent on institutional context. A lack of standardized outcome reporting and platform heterogeneity limits cross-study comparisons. Ongoing, long-term, multicenter randomized trials are expected to address these evidence gaps. Robotic assistance can improve the efficiency and precision of arthroplasty procedures, with the potential for substantial cost-effectiveness when optimized. Institutions adopting this technology can expect improved outcomes by leveraging local surgical volume and infrastructure. Wider integration will be facilitated by robust long-term data, standardized outcome metrics, and continuous innovation that aligns with value-based care models.