<p>Cycle‑to‑cycle variations (CCV), caused by fluctuations in combustion between successive engine cycles, directly influence the performance, emissions, and reliability of compression ignition engines, often leading to power output inconsistencies, higher emissions, and added mechanical stresses. The growing demand for environmental sustainability has led to the transition towards alternative fuels and low‑temperature combustion strategies, which often operate near the critical 5% stability threshold and thus require precise characterization of cyclic variability to ensure the development of effective engine technologies. This review offers a detailed analysis to explore the impact of cyclic variations in internal combustion engines with a focus on the responsible core mechanisms, identifying the role of physical mixing timescales and chemical ignition delays, as well as key contributing factors and transient conditions while outlining effective mitigation strategies. The progress in characterization methods is examined by comparing conventional methods with emerging diagnostic tools such as time–frequency analysis, nonlinear dynamics techniques, and data-driven models, as these offer improved capability to identify multiscale, non-stationary behavior in combustion signals and to detect early indicators of unstable cycles. A critical assessment of experimental and numerical studies shows that appropriate control of injection timing, combustion phasing, and in-cylinder flow structures can reduce the COV_Imep by approximately 10% to 40%, depending on engine load and combustion mode. While oxygenated and alcohol-based fuels consistently lower soot and CO emissions, they often induce higher CCV under highly dilute conditions, indicating a clear trade-off between stability and emissions control. Advanced analysis techniques including wavelet-based methods, and machine-learning predictors, demonstrate strong potential for capturing these dynamics for real-time mitigation of unstable combustion cycles. Remaining challenges include establishing unified metrics for assessment and integrating physics-based machine-learning models for transient multi-cylinder validation. By linking the responsible mechanisms, diagnostic methodologies, and control strategies, this review provides a coherent framework to guide the development of next-generation engines with improved combustion stability.</p>