Child sexual exploitation has become a critical issue exacerbated by the rise of internet usage, exposing children to various online vulnerabilities. Effective combatting of online child sexual exploitation necessitates a multi-stakeholder approach involving governments, law enforcement, technology companies, and civil society. The prevalence of Child Sexual Abuse Material (CSAM) online is alarming, with millions of instances reported annually. For example, the National Center for Missing and Exploited Children (NCMEC) received 21.7 million reports of suspected CSAM in 2020 alone, illustrating the vast scope of this issue. Effective combatting of online child sexual exploitation necessitates a multi-stakeholder approach involving governments, law enforcement, technology companies, and civil society. Current efforts to combat this issue include the implementation of technological tools for detection, international cooperation for law enforcement, and the establishment of stricter legal frameworks. However, these efforts are continually challenged by the evolving nature of online exploitation and the limitations in existing strategies. This paper explores the complexities in addressing the issue related to investigation of CSAM cases, discussed the implementation of AI leveraged solution to solve complex Law enforcement problem also focusing on the trade-offs between privacy and online safety, the challenges of legal jurisdiction, and the tension between proactive and reactive measures. The emergence of generative AI technologies, such as Large Language Models (LLMs), Diffusion Models, Generative Adversarial Networks (GANs), and Neural Radiance Fields (NeRFs), further complicates the landscape by facilitating the creation and distribution of online child sexual abuse material (CSAM). This paper identifies significant problems, including the rise of AI-generated CSAM, the misuse of uncensored open-source AI models, and the monetization of CSAM on social media platforms. Case studies, such as CBI INDIA’s Operation CARBON, highlight the scale and challenges of combating online child sexual exploitation. The application of data science techniques, including content analysis, pattern recognition, object detection, face recognition, and social media network analysis, offers promising investigative solutions to these challenges. However, these technological advances must be balanced with ethical considerations and strict regulatory measures to protect personal privacy and civil liberties while effectively safeguarding children.