Reliability assurance remains a central challenge in the development of liquid-fueled rocket engines, where extreme operating environments, complex coupled physics, and stringent performance requirements intersect with programmatic constraints on cost and schedule. Traditional approaches based on deterministic safety margins and extensive test campaigns have achieved high levels of mission success but are increasingly strained by the demand for rapid development cycles and novel propulsion architectures. This article synthesizes classical reliability concepts, contemporary uncertainty quantification methodologies, and emerging data-driven techniques into a unified, reliability-driven development framework for liquid-fueled rocket engines. Drawing upon established standards and foundational studies, the manuscript examines the evolution from margin-based design philosophies toward probabilistic, limit-state, and Bayesian approaches that explicitly represent epistemic and aleatory uncertainties. The analysis integrates insights from structural reliability, cost estimation under technical uncertainty, and accelerated test-based development strategies. Particular attention is given to the role of uncertainty propagation, stochastic material characterization, and deep learning–assisted reliability analysis as complementary tools rather than replacements for physics-based understanding. The discussion highlights how reliability metrics can be systematically linked to development decision-making, verification planning, and risk-informed trade studies without asserting deterministic causality. Limitations related to data scarcity, model credibility, and organizational adoption are critically examined. The article concludes by outlining a pathway for integrating uncertainty-informed reliability assessment into future liquid propulsion programs, emphasizing transparency, traceability, and alignment with existing aerospace standards. The proposed synthesis contributes to the ongoing discourse on how reliability-driven frameworks may support sustainable, predictable, and defensible rocket engine development practices.

Liquid rocket engines, reliability engineering, uncertainty quantification, probabilistic design, Bayesian inference, propulsion system development

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