en Applications Applications پژوهشي Research <span style="font-size:11.5pt"><span new="" roman="" style="font-family:" times="">This study develops a synthetic earthquake catalog for Iran (1900&ndash;1963) using a deep neural network (DNN) optimized by the Enhanced Colliding Bodies Optimization (ECBO) algorithm. The model, trained on post-1964 instrumental data from the Iranian Seismological Center, incorporates spatial, temporal, and tectonic features to estimate earthquake magnitudes. Statistical indices (MAE = 0.0064; RMSE = 0.3748) and bootstrap uncertainty analysis (&plusmn;0.18 M) confirm the model&rsquo;s reliability. The generated catalog provides a data-driven basis for improving seismic hazard assessment and historical seismicity reconstruction across the Iranian plateau.</span></span> Earthquake data reconstruction, Deep neural network, ECBO algorithm, Seismic hazard assessment, Iran, Historical seismicity 93 106 http://ijoce.iust.ac.ir/browse.php?a_code=A-10-6463-47&slc_lang=en&sid=1 A. Kaveh Alikaveh@iust.ac.ir 180031947532846002903 180031947532846002903 No School of Civil Engineering, Iran University of Science and Technology, PO Box 16846-13114, Iran A. Beitollahi 180031947532846002904 180031947532846002904 No Housing and Urban Development Research Center, PO Code 463917151, Iran N. Khavaninzadeh khneda92@gmail.com 180031947532846002905 180031947532846002905 Yes School of Civil Engineering, Iran University of Science and Technology, PO Box 16846-13114, Iran