Recent advancements in speech processing have led to the development of intelligent systems for automatic transcription, audio classification, and multilingual translation. This paper presents a unified framework for speech-to-text (STT), text-to-speech (TTS), and gender detection using advanced machine learning and deep learning techniques. Key components include MFCC analysis, noise reduction, and contextual understanding to ensure accurate transcription and translation across multiple languages. It gets a Word Error Rate of 3.5%, Signal-to-Noise Ratio of 24.3 dB, with an MOS of 4.5 as compared to models such as DeepVoice and Tacotron. The framework is trained with varied speech samples from speakers of diverse ages, genders, and accents, enhancing its robustness and scalability. Augmentation techniques in the form of noise injection and pitch scaling improve generalization for real-world applications. For gender classification, annotated datasets enable models to discover gender-specific traits based on features like pitch range, mean pitch, and variability. Besides that, the system supports multilingual translation, by transcribing speech into text and then translating the latter into several languages, after which the synthesized text is given out as audio. That makes the framework highly applicable to multilingual real-world cases and provides a solution that is holistic enough for tasks concerning speech processing.