<p>Accurate subtyping of acute leukemia is essential for guiding therapy and predicting patient outcomes. Morphological assessment remains challenging for distinguishing subtypes with subtle cytomorphologic differences, particularly in rare or atypical forms where reliable classification is limited. Recent computational models have attempted to automate this process. However, their clinical applicability was limited by insufficient generalizability and granularity across subtypes of acute leukemia. Here we developed a deep learning framework for automated cell-level classification and case-level subtyping of acute leukemia from Wright-Giemsa-stained bone marrow smears. The model was trained on 180,928 expert-annotated single-cell images representing 19 hematopoietic and leukemic cell categories collected from three different imaging platforms to enhance generalizability. ALSNet incorporates a dual-branch convolutional architecture and a Transformer encoder to capture both fine-grained local features and global morphological context. Internally, ALSNet achieved per-class accuracies up to 0.99 for mature cells and &gt; 0.80 for diagnostically relevant precursors, while in an external validation from an independent platform, case-level accuracy reached 0.75 with leukemic cell percentage strongly correlated to manual review (<i>R</i>² = 0.66). These results indicate that ALSNet enables robust, platform-independent morphological classification and may facilitate the early, reliable diagnosis of acute leukemia in clinical practice.</p>