This study evaluates the effectiveness of Bank Indonesia's communication strategy by integrating computational linguistics, media sentiment analytics, and macroeconomic diagnostics within a unified empirical framework. Using advanced Natural Language Processing (NLP) techniques, BI's press releases from 2019–2024 are transformed into quantitative indicators capturing clarity, sentiment, comprehensiveness, consistency, and economic appropriateness. In parallel, news articles on inflation and exchange rate developments are analyzed to assess how policy messages are transmitted or amplified through media channels. These linguistic features are further enriched using Named Entity Recognition to identify stakeholder-specific resonance and potential pathways of narrative distortion within the public communication ecosystem. To assess macroeconomic implications, a VARX model links communication characteristics to intermediary channels, market expectations, and macroeconomic outcomes under both normal and anomalous conditions. Complementing this analysis, an Early Warning System (EWS) employing a 12 month rolling window and IsolationForest anomaly detection identifies periods of inflation and exchange-rate stress, providing a diagnostic foundation for anticipating heightened communication demands. The findings show that central bank c​ommunication functions not only as an information conduit but also as an active policy instrument that shapes expectations and influences market behavior. Building on these insights, the study proposes a three-pillar framework: Features, Timing, and Channels; to strengthen clarity, responsiveness, and coherence in central bank communication. This research advances the literature by integrating AI/ML-based diagnostics with policy communication analysis, offering an empirically grounded approach to enhancing communication effectiveness, transparency, and expectation management.