Prior computational work on Linear A has focused on isolated statistical models with little cross-linguistic integration. This study introduces a unified computational framework analyzing Linear A, Linear B, and Swahili through probabilistic modeling and pattern mining. We construct syllable-level Markov models, extract transition probability matrices, and apply Jaccard similarity and Apriori rule mining to uncover structural patterns. Results reveal recurring transition clusters in Linear A suggestive of morphological structure and identify significant syllabic co-occurrences in Swahili that align with patterns in ancient scripts. Cross-script analysis highlights potential phonotactic and positional correspondences, offering new computational insights into Linear A’s linguistic organization. The methods presented can help in the decipherment of scripts that lack bilingual texts.