Education is evolving, yet traditional learning models struggle to adapt to individual student needs. Synapseed is an AI-driven, innovative learning system that personalizes education through Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent AI frameworks. It dynamically tailors learning paths, providing real-time AI-driven content, interactive explanations, and coding assistance across various programming languages. The system integrates vector databases, conversational memory models, and CrewAl-powered multi-agent systems to enhance knowledge retrieval from PDFs, YouTube transcripts, and structured academic resources. Additionally, Code Synapse offers multi-language coding support with syntax-aware responses. The platform is scalable and suitable for K-12, higher education, and professional training. A key innovation of SynapseEd is its efficient memory utilization and computational optimization. Unlike traditional LLM-based educational models that require high-end GPUs and large-scale infrastructure, SynapseEd achieves similar functionality on a non-GPU system with just 8GB of RAM. Leveraging quantization (4-bit QLoRA) and lightweight fine-tuning techniques demonstrates that LL.M-powered educational platforms can be built within limited hardware constraints, making AI-driven learning more accessible and deployable across diverse environments.