The study focuses on the Makran Trench in the Arabian Sea basin, in the north Indian Ocean. The area is tectonically active, with a system of ridges and fracture zones morphologically separating the Arabian Sea. The study examined the relationships between the topographic structure of the Makran Trench and the regional settings of the Arabian Sea: geomorphology, sediment thickness, geophysical fields, geology and tectonic lineaments. The methodology is based on the GMT scripting toolset. The spatial analysis includes high-resolution datasets GEBCO, EGM2008, GlobSed and data on tectonics, geology, geophysics, sediment thickness and topographic terrain model visualized by GMT. The paper also defined a way in which the proprietary ESRI data format can be transformed into the freely available GMT geospatial data of the geoid EGM2008 model. The geomorphological modeling included the automatic digitization of 300-km width cross-section profiles of the trench demonstrating its submarine relief. The analysis showed a correlation between the geological and tectonic structures, asymmetric geomorphology and geophysical anomaly fields. Gravity data indicate a crustal structure with anomalies generated by the bending of the lithosphere into the Makran subduction zone and density variations in the mantle reflected on the gravity maps. The gravity correlates with lineaments of the geomorphic structures. Bathymetric analysis revealed the most frequent depth (448 samples) at −3,250 to −3,500 m, followed by intervals: −3,000 to −3,250 m, −2,750 to −3,000 m. The declining continental slope correlates with gradually decreasing depths as equally distributed bins: 124 samples (−2,500 to −2,750 m), 96 (−2,250 to −2,500 m), 86 (−2,000 to −2,250 m). The trench is an asymmetric form with a high steepness on the continental slope of Pakistan and low steepness with a flat valley on the oceanward side. The multi-source data integration is important for seafloor mapping and the geomorphological analysis of oceanic trenches hidden to direct observations. The machine learning methods of GMT and cartographic modeling provide possibilities for the effective visualization of the trench. The comparison of the geomorphology with gravity anomalies, tectonic lineation, geological structures and topographical variations provides more detail to studies of the seafloor in the Arabian Sea.