Mr. Muhammad Ateeq’s research integrates computer science, machine learning, and remote sensing to deliver scalable, data-driven solutions for Earth observation and energy planning. His work advances multi-sensor data fusion, image segmentation, and environmental change detection, with strong emphasis on computational rigor and real-world applicability. A notable contribution is his scenario-based spatial assessment framework for hybrid solar–wind energy systems, combining geospatial analytics with techno-economic modeling to support resilient renewable infrastructure planning. His research also extends to deep learning–based plant disease detection, demonstrating high-accuracy classification using transfer learning, and to network security analysis in next-generation communication systems. Collectively, these works highlight methodological versatility and cross-domain relevance. As reflected in his ORCID profile, he has 2 indexed journal publications, an emerging h-index, and a growing citation record, underscoring increasing scholarly visibility and impact.