Prosodic boundary prediction from text remains underexplored for morphologically rich languages such as Hindi. We present a comprehensive study of text-based prosodic boundary prediction for Hindi children’s texts using interpretable linguistic representations. We introduce a new corpus of 54 textbook passages annotated by six fluent Hindi speakers and formulate two complementary tasks: Prosodic Boundary Detection (PBD) and Forbidden Pause Detection (FPD). Analysis of the corpus shows that 42.6% of majority-supported boundaries lack explicit punctuation, motivating the use of richer linguistic features beyond surface cues. Using a 28-feature linguistic representation, our models achieve F₁ scores of 0.89 (PBD) and 0.93 (FPD), while generalizing to an independently annotated Hindi corpus without retraining. We study the optimal feature set in terms of its differences from a previously published similar work in English, and also investigate the cross-lingual portability of the feature sets to draw insights that are supported by the structural differences between Hindi and English.