Long-acting injectable microspheres are an important platform for sustained drug delivery because they can maintain therapeutic exposure over extended dosing intervals. Their release behavior remains difficult to predict because polymer degradation, drug loading, and microsphere morphology interact across burst, lag, and erosion-controlled phases. Current formulation development often depends on empirical iteration, in which candidate batches are manufactured and tested before mechanistic understanding is complete. This trial-and-error workflow can slow development when small changes in polymer grade, drug distribution, or particle structure alter the full release profile. The objective of this predictive-model article is to describe a machine learning framework for forecasting the in-vitro release curve of long-acting injectable microspheres from formulation and morphology descriptors. The same framework could be extended conceptually to estimate in-vivo pharmacokinetic behavior when suitable bridging data are available. A gradient-boosted tree or multi-output regression model would be trained on curated long-acting injectable formulation records. Inputs would encode polymer chemistry and degradation properties, drug loading and physicochemical features, and quantitative microsphere morphology descriptors such as particle size, porosity, surface area, and internal structure. Conceptually, the model could predict the release profile under different formulation and processing conditions while ranking the relative influence of polymer, drug, and morphology features. Such predictions would be expected to support virtual screening of formulation variants before experimental confirmation. A model-informed formulation strategy could help shift long-acting injectable microsphere development from empirical testing toward rational selection of polymer, drug-loading, and morphology targets. This approach should be evaluated prospectively before being used to support high-impact development or regulatory decisions.