With an indisputable complexity of communication for hearing and speaking impaired people, most sign language recognition systems utilize virtual reality or onscreen robots. This paper presents the design and development of a special and low-cost humanoid robot that can perform as a sign language interpreter. To the best of our knowledge, this is the first endeavor to fabricate a humanoid robot for Bangla sign language (BdSL) and Medical signs interpretation. Considering the plethora of design criteria and balancing between rigidity and flexibility 3D models of the robotics parts are designed and 3D printed ensuring cost efficiency. With the help of modern fabrication technology, the robot is developed and assembled with proper actuators and circuitry. An image dataset is built comprising 950 images for BdSL recognition and made publicly available. We utilized the Recurrent neural network (RNN) and Convolutional neural network (CNN) for deep learning model establishment and feature extraction from video and image data. The developed humanoid robot has 43 Degrees of freedom (DoF) which includes two 15 DoF hands. It can imitate 16 BdSL alphabets in sign language, can capture a video or image input in real-time from the user, and recognize 10 medical signs and 38 alphabets of BdSL. The learning model for video-based medical sign recognition achieved 87.5% test accuracy. Image-based Bangla sign language recognition achieved an overall test accuracy of 98.19% in our dataset and 93.8% in another available dataset. Compared to the state-of-the-art robotic systems for sign language interpretation, our approach has achieved higher kinematic characteristics, remarkable results in sign recognition, and impressive competency in sign imitation; all at almost 10 times lower cost than the state-of-the-art systems. The results are evidence that our approach is efficient and suitable in helping hearing and speaking impaired people. Moreover, this work initiates a research scope that can be further extended for creating equal opportunities for the hearing and speaking impaired community.