The swift advancement of 5G networks demands creative solutions to address the dynamic and diverse requirements of modern wireless communication. In this investigation, we delve into an innovative approach to improve resource allocation by integrating Convolutional Neural Network (CNN)-based femtocell management with network slicing techniques. The suggested system takes advantage of the intrinsic flexibility of network slicing to create isolated and tailored slices customized to meet specific application demands. The inclusion of CNN algorithms in femtocell management amplifies the adaptability and intelligence of the system. By conducting thorough simulations and real-world experiments, our study evaluates the performance enhancements derived from this integrated approach. The outcomes demonstrate a noteworthy improvement in Quality of Service (QoS) metrics, including decreased latency, increased throughput, and enhanced resource utilization efficiency. The adaptive characteristics of CNN-based femtocell management play a crucial role in dynamically adapting to varying network conditions and user demands. This study not only progresses the optimization of resource allocation in 5G networks but also emphasizes the possibilities of integrating network slicing and machine learning to achieve intelligent femtocell management. The valuable insights derived from our findings provide guidance for network operators, researchers, and industry stakeholders as they navigate the challenges presented by the continually evolving 5G landscape.