With the increasing adoption of digital education, students often face difficulty in capturing important information during lectures while focusing on understanding the content. Manual note-taking can lead to incomplete or inaccurate documentation of lecture material. This research presents an AI-powered system that automatically converts lecture audio into structured learning resources. The proposed system integrates speech recognition, natural language processing, and keyword extraction techniques to generate lecture summaries and concept maps. Speech-to-text conversion is performed using automatic speech recognition models, while summarization algorithms extract key ideas from the transcript. Concept mapping visually represents relationships between the main topics discussed in the lecture. The system improves learning efficiency by helping students quickly review lecture content and identify key concepts.