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Relapse remain the leading cause of treatment failure after allogeneic hematopoietic stem cell transplantation (allo-HSCT) in patients with acute myeloid leukemia (AML), despite substantial advances in transplant strategies and supportive care. The dynamics of immune reconstitution (IR) critically determine post-transplant outcomes by shaping the balance between graft-versus-leukemia (GvL) effects, graft-versus-host disease (GvHD), infectious complications, and leukemic immune escape. Importantly, IR is not limited to numerical recovery of immune cells but represents a multidimensional and temporally organized process encompassing quantitative, qualitative, and functional immune restoration. In this review, we provide an integrated clinical laboratory-oriented framework for immune monitoring (IM) after allo-HSCT, with a specific focus on relapse prediction and risk stratification in AML. We discuss the sequential kinetics of innate and adaptive immune recovery, key cellular subsets influencing GvL efficacy, and the impact of transplant-related factors, immunosuppression, and viral reactivations on IR trajectories. Particular emphasis is placed on functional immune states, including T-cell exhaustion, anergy, and senescence, as measurable laboratory correlates of impaired immune surveillance and impending relapse. We further outline current IM methodologies used in routine and advanced clinical laboratories, including multiparameter flow cytometry, measurable residual disease (MRD) assessment, immune repertoire analysis, and emerging omics-based approaches. By integrating immunophenotypic, molecular, and functional data, IM enables earlier detection of relapse-associated immune dysfunction and supports preemptive, risk-adapted therapeutic interventions such as donor lymphocyte infusion or immunomodulatory strategies. Overall, this review highlights the pivotal role of comprehensive, longitudinal immune monitoring in translating complex immunological data into clinically actionable insights. Expanding IM beyond conventional parameters toward integrated, multidimensional approaches is essential for improving relapse prediction, personalizing post-transplant management, and ultimately enhancing long-term outcomes in AML patients undergoing allo-HSCT.