In response to the growing demand for seamless connectivity and enhanced network performance, traditional cellular systems face challenges such as data overload, spectrum scarcity, and power inefficiencies. Business mobile users increasingly expect prompt and high-quality connectivity across various devices, relying on public 4G and 5G networks. The rise of video consumption and other services and the adoption of Virtual Reality and Augmented Reality for collaboration, training, and remote work further strain network infrastructure, impacting users’ Quality of Experience (QoE) and the network’s Quality of Service (QoS). To address these challenges, Heterogeneous Networks (HetNets) in 5G and small cells like femtocells are deployed to alleviate macrocell congestion. However, they encounter obstacles such as interference and traffic offloading. Introducing Machine Learning (ML) into the 5G architecture presents a promising solution. This work discusses HetNets and the issues they face, including interference, resource allocation, and traffic offloading. Firstly, we propose ML-based solutions like Machine-Learning Network Selector (ML-NETSEL), which automatically selects the best base station in a 5G/LTE environment to optimize traffic offloading and meet QoS requirements. Secondly, Machine Learning Interference Classification and Offloading Scheme (MLICOS) classifies users’ traffic based on experienced co-tier interference levels using binary classification and offloads affected traffic to nearby femtocells, aiming to enhance QoE and network QoS. Additionally, Machine Learning Multi-Classification and Offloading Scheme (MLMCOS) mitigates co-tier interference in 5G HetNets by classifying users into multiple classes and prioritizing offloading based on service requirements. Finally, Machine Learning Enhanced Classification for Interference Management and Offloading (MLCIMO) enhances QoS and QoE by categorizing users based on interference types and offloading them accordingly, utilizing a multi-binary classification approach. These ML-based solutions leverage real-time network conditions to optimize both QoS and QoE. Rigorous simulations validate their effectiveness across various scenarios, highlighting their potential to address contemporary network challenges and contribute to a more resilient and efficient 5G ecosystem.