The project "Real-Time Indian Sign Language Recognition and Speech Translation with Analytics using MediaPipe and LSTM" is designed to create an intelligent system that bridges the communication gap between deaf individuals and the hearing community by translating Indian Sign Language (ISL) gestures into spoken language in real time. This system uses the INCLUDE-50 dataset from Kaggle, which contains a large collection of Indian Sign Language video samples organized class-wise for each gesture. The workflow begins with live video capture via a webcam, where MediaPipe Hands detects and extracts 21 three-dimensional hand landmarks (x, y, z coordinates) for each frame, representing finger positions and gestures. These landmark sequences are processed and fed into a Long Short-Term Memory (LSTM) deep learning model, chosen for its ability to learn temporal dependencies in sequential data, to classify gestures accurately. Once classified, the gesture is mapped to its corresponding text and converted into speech using a text-to-speech engine such as pyttsx3 or Google TTS, enabling seamless communication. Alongside recognition, the system includes an analytics module that tracks gesture frequency, recognition accuracy, and system performance, visualizing data through charts and dashboards using libraries like Matplotlib or Plotly. Implemented using Python with OpenCV for video processing, NumPy for numerical computation, and TensorFlow/PyTorch for LSTM model training, the system aims to deliver real-time recognition with high accuracy and low latency (<200 ms). This project addresses challenges such as variations in gesture execution, occlusion, and computational demands by optimizing landmark extraction and model architecture. Future extensions include expanding the system to full-sentence recognition, multilingual translation, mobile deployment, and emotion recognition to enhance communication. This project demonstrates a strong combination of computer vision, deep learning, natural language processing, and accessibility technologies, providing a socially impactful tool for inclusive communication.