Crypto currencies are receiving a lot of attention from media due to its importance. Several papers have similar prices but some others don't, but the time complexity of those forecasts is greater, so we use an artificial intelligence-linked algorithm called the least absolute shrinkage selection operator here in this paper to reduce the time complexity. While there have been several prior reports, the larger part of them has focused entirely on the behaviour of Bitcoin. In this paper, we propose using regular AI devices and accessible online networking information to predict the development of value of Bitcoin, Ethereum, Ripple and Litecoin’s digital currency showcase developments. We are also using the neural networks, SVM and Random Forests while analyzing the components from twitter Market. The results demonstrate that it is possible to forecast cryptographic money markets utilizing AI and hypothesis checking, where Twitter information may be used to anticipate certain cryptocurrencies without someone else, and NN can outflow various models.