Acute myeloid leukaemia (AML) is characterised by expansion of immature cells of the myeloid lineage in the bone marrow. Almost half of paediatric AML patients relapse after standard treatment. Personalised medicine—including immunotherapies—could target chemotherapy-resistant cells and achieve long-term remission. However, identifying targets for AML immunotherapy is complicated by patient heterogeneity, complex disease evolution, and similarity of aberrant and developing cells. Therefore, we planned to identify malignant cells and place them along the myeloid developmental trajectory using machine learning. We generated single-cell flow cytometry profiles of 20 paediatric AML patients with matched samples at diagnosis, remission, and relapse. With this dataset, we trained an auto-encoder on cells of remission samples (Fig. 1B&2A). We show that the auto-encoder’s latent space captures the healthy developmental trajectory (Fig. 3A). Then, we projected all samples onto this trajectory and identified their developmental stages (Fig. 3B&C). Furthermore, classifying malignant cells using the auto-encoder's reconstruction error achieved 96% accuracy (Fig. 4B&C). Finally, KMT2A-mutated AMLs changed their phenotype drastically from diagnosis to relapse (Fig. 5A&B). Summarising, we identify malignant cells and developmental stages in AML samples. We uncover phenotypic changes related to mutation status. Our work could aid researchers investigating immunotherapy targets to improve AML treatment.