Earthquake forecasting is a topic of utmost societal importance, yet has represented one of the greatest challenges to date. Case studies from the past show that seismic activity may lead to changes in the local geomagnetic and ionospheric field, which may operate as potential precursors and postcursors to large-magnitude earthquakes. However, detailed and data-driven research has yet to support the existence of precursors and postcursors. This work makes an attempt to build data-driven deep learning networks that can learn the temporal changes in geophysical phenomena before and after large magnitude earthquake events. First, we do numerous experiments using various machine learning and deep learning models, but none of them are sufficiently generalizable to forecast earthquakes from potential precursors. Our negative findings may make sense as there is not any conclusive and comprehensive evidence yet supporting the existence of earthquake precursors. We, therefore consider detecting earthquakes from postcursors data to spot potential pitfalls and outline the scope of possibility. Our tests indicate that while detecting earthquakes from postcursor data might be promising, it would fall short. Poor performance could be brought on by a lack of data and extremely complex relationships. However, we are leaving room for future research with deeper networks and data augmentation.