Real-time speech recognition has evolved dramatically with the introduction of deep learning architectures, enabling high accuracy, low latency, and robust performance across diverse acoustic conditions. This paper provides a comprehensive review and proposed framework using state-of-the-art models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Transformers, and end-to-end architectures like DeepSpeech and wav2vec 2.0. A complete system workflow, block diagrams, algorithmic steps, results, and conclusions are also presented. These models enable efficient parallelization, improved context modeling, and robust performance under real-world noise conditions, making them suitable for applications such as AI assistants, streaming transcription services, conversational AI, navigation systems, and edge-deployed embedded devices. Despite these advancements, achieving real-time performance remains challenging due to factors such as inference latency, memory footprint, streaming complexity, and the difficulty of processing long utterances in low-resource environments. This paper presents a comprehensive study of state-of-the-art deep learning architectures for real-time Automatic Speech Recognition (ASR), highlighting their design principles, computational characteristics, model variants, and deployment considerations. A detailed analysis of Conformer and RNN-T based streaming systems is provided, along with illustrations, data flow diagrams, and experimental insights. The paper also discusses ongoing challenges—including multilingual adaptation, noise robustness, and on-device model optimization—and outlines future research directions toward more efficient, scalable, and human-level real-time speech recognition systems.