Background: Background: Cardiovascular diseases account for approximately 17.9 million deaths globally each year (32% of total mortality). Early prediction of major adverse cardiovascular events (MACE) is essential for improving outcomes. The lymphocyte-to-high-density lipoprotein ratio (LHR), which reflects systemic inflammation and dyslipidemia, has recently emerged as a potential prognostic biomarker. Objective: To systematically review and meta-analyze the prognostic value of LHR in predicting MACE among patients with cardiovascular disease. Methods: A comprehensive search of PubMed, Embase, Scopus, Web of Science, and Cochrane Library was performed up to June 2025, following PRISMA 2020 guidelines. Eligible studies evaluated the association between LHR and MACE, reporting hazard ratios (HRs) or odds ratios (ORs). A random-effects model was used to calculate pooled effect sizes. Heterogeneity was assessed using I² statistics, and publication bias using Egger’s test. Results: A total of 12 studies (n = 14,673 patients) were included. High LHR was associated with a significantly increased risk of MACE (pooled HR = 1.58; 95% CI: 1.35–1.83; p < 0.001; I² = 46%). Subgroup analysis showed stronger prognostic value in acute coronary syndrome (ACS) (HR = 1.72; 95% CI: 1.41–2.09) and patients undergoing percutaneous coronary intervention (PCI) (HR = 1.65; 95% CI: 1.30–2.08). Elevated LHR also predicted higher risk of mortality (HR = 1.42; 95% CI: 1.17–1.72; p = 0.002) and myocardial infarction recurrence (HR = 1.67; 95% CI: 1.29–2.15; p < 0.001). Sensitivity analysis confirmed the stability of estimates, and no significant publication bias was detected (Egger’s p = 0.21). Conclusion: Elevated LHR is a strong, independent predictor of major adverse cardiovascular events. As an inexpensive and readily available measure derived from standard blood tests, LHR has significant potential for integration into existing cardiovascular risk stratification models. Prospective multicenter trials are recommended to validate findings and determine optimal clinical cut-off values.