Multi-ionic materials pose a distinct representational challenge in machine learning-driven materials design. Different from single-molecule or composition-based materials, their properties arise from how charged building blocks aggregate into specific assemblies. Here, we show how pretrained machine-learned interatomic potentials (MLIPs) can bypass full crystal-structure prediction and support pre-synthesis screening from stoichiometric ionic clusters using multi-ionic integrated explosives (MIXs) as a synthesis-facing example. This strategy combines a stoichiometric ionic-cluster representation, which represents each candidate material by a non-periodic, stoichiometry-preserved formula-unit cluster, with multi-task fine-tuning (MT-FT), which adapts a pretrained atomistic backbone while retaining the energy–force objective as physical regularization for the sparse detonation-velocity labels. With the pretrained backbone regularized by MT-FT, this surrogate provides a cross-validated screen across only 25 structurally curated perovskite-type energetic materials (PEMs) with experimentally derived Kamlet–Jacobs (K–J) detonation velocities. Representation probes show that the learned descriptors implicitly retain site-aware ionic organization, density information, and coarse packing compatibility, implying why non-periodic clusters can remain predictive before full crystal structures are known. The surrogate extends known PEMs chemistry to three newly synthesized ABX₄ materials with both unseen ABX₄ stoichiometry and an unseen ethylenediammonium B-site cation, yielding three-point concordance with K–J reference velocities and a mean absolute error (MAE) of 92 m·s⁻¹ without retraining. Together, these results establish stoichiometry-preserved cluster learning as a synthesis-facing screening strategy for data-scarce multi-ionic materials.