<p>This paper introduces a novel automated framework aimed at bridging the gap between software design documentation and practical project management schedules. The primary objective is to address the longstanding challenge of manually translating UML sequence diagrams, fundamental design artifacts in software engineering, into executable and accurate project management plans such as Gantt charts and precedence graphs. Current manual approaches to schedule generation are notably error-prone, time-consuming, and inadequately integrated with the dynamic requirements of Agile and iterative project environments. To tackle this critical research gap, the paper proposes an integrated methodological pipeline combining advanced Optical Character Recognition (OCR), dependency graph generation, and machine learning optimization techniques. Specifically, OCR is employed to automatically extract tasks, interactions, and temporal details directly from visual representations of UML sequence diagrams, significantly reducing transcription errors and saving valuable planning time. Following extraction, tasks are structured into a validated Directed Acyclic Graph (DAG), accurately modeling inter-task dependencies and constraints. Further enhancing scheduling accuracy, the study applies gradient descent methods to iteratively predict and optimize task durations, moving beyond static estimates to dynamically refined predictions based on real-world project constraints. A forward pass analysis then calculates the earliest feasible start times, while Critical Path Method (CPM) analysis identifies the tasks crucial for project completion timelines. Comprehensive experimental validation across diverse scenarios clearly demonstrates the effectiveness and reliability of the proposed framework. Results show notable improvements in scheduling precision, visualization clarity through8/ generated Gantt charts and precedence graphs, and offering practical benefits to project managers and software development teams.</p>