Traditional personal journaling provides a critical medium for cognitive offloading, yet digital alternatives remain constrained by a passive data lifecycle where unstructured entries reside in isolation from an individual’s productivity workflows. This paper presents LifeScript, a system designed to map qualitative retrospective text into prioritized tasks via a multi-tiered natural language processing framework. The architecture integrates a fine-tuned transformer for contex-tual emotion classification with a neural token classification pipeline to extract behavioral intents, goals, and habits. Ad-ditionally, a heuristic parsing engine normalizes colloquial temporal markers into structured task metrics within a cross-platform client dashboard. Empirical evaluation demonstrates that the framework achieves an entity-level F1-score of 86.3% in intent extraction alongside a System Usability Scale score of 84.5 6.2. These outcomes demonstrate the practical viability of operationalizing natural prose diaries into execution-focused data tracking pipelines, establishing a resource-efficient architecture for personal knowledge management systems.