Communication forms the basis of information exchange between individuals. The process of conveying one's thought processes depends on the different types of communication, orality being the direct and highly effective option. Around, 5 to 10 percent of the population is not equipped with this necessity. This brings in the need for alternativeslike sign language. Sign Language employs the visual manner of expression. There are over 300 sign languages across the world, and furthermore, variations stemmed from the regionalization of accents. This confers to the issue of difficulty in understanding speech and hearing-impaired people across the world. Barriers within accents of the same language are characteristic. This paper works at trying to bridge this gap in communication, by using Machine Learning to create a sign-language translation system that converts gestures to the spoken word.