  Earthquake prediction has been a challenging research area for many decades,
where the future occurrence of this highly uncertain calamity is predicted. In
this paper, several parametric and non-parametric features were calculated,
where the non-parametric features were calculated using the parametric
features. $8$ seismic features were calculated using Gutenberg-Richter law, the
total recurrence, and the seismic energy release. Additionally, criterions such
as Maximum Relevance and Maximum Redundancy were applied to choose the
pertinent features. These features along with others were used as input for an
Extreme Learning Machine (ELM) Regression Model. Magnitude and time data of $5$
decades from the Assam-Guwahati region were used to create this model for
magnitude prediction. The Testing Accuracy and Testing Speed were computed
taking the Root Mean Squared Error (RMSE) as the parameter for evaluating the
mode. As confirmed by the results, ELM shows better scalability with much
faster training and testing speed (up to a thousand times faster) than
traditional Support Vector Machines. The testing RMSE came out to be around
$0.097$. To further test the model's robustness -- magnitude-time data from
California was used to calculate the seismic indicators which were then fed
into an ELM and then tested on the Assam-Guwahati region. The model proves to
be robust and can be implemented in early warning systems as it continues to be
a major part of Disaster Response and management.
