Radar technology has seen burgeoning applications across diverse domains, including automotive industries and surveillance systems, thereby intertwining with everyday life. Concurrently, the escalating challenge posed by space debris underscores the critical need for innovative solutions. Ground-based radars have historically addressed aspects of this challenge; however, airborne platforms are gaining prominence. Yet, the scarcity of publicly available data impedes rapid advancements in this field. Harnessing the power of deep learning presents a promising avenue for enhancing radar capabilities, potentially revolutionizing object recognition and predictive accuracy beyond traditional methodologies. This study introduces two novel data generation methods aimed at mitigating data scarcity and explores the transformative potential of deep learning in radar-based object recognition and prediction.