Hybrid manufacturing has emerged as an advanced manufacturing technique to fabricate complex, critical components with precision and ensure functional performance. Such technologies face some obstacles, such as process variability, defect formation and insufficient real-time adaptiveness. This study proposes an intelligent framework to mitigate such limitations by incorporating an ML-driven real-time monitoring tool for process control, including defect identification and quality optimisation. This approach integrates multi-sensor in situ monitoring tools during the fabrication process, such as temperature sensors, visual sensor data and process-related vibration data. A convolutional neural network (CNN) is utilised to identify spatial attributes associated with surface characteristics and defect patterns during the process. A long short-term memory (LSTM) network is employed to capture time-dependent relationships within process signals that are combined with a CNN to ensure in situ defect identification and predict process quality status, including surface integrity and trends in mechanical properties. Handcrafted statistical feature extraction and smart anomaly-driven image features inspired by activity recognition are utilised in the LSTM network to identify predefined types of anomalies. This ML-driven framework develops an adaptive control strategy to execute the real-time opitimisation of critical parameters, such as energy consumption, feed rate, heat input and tool path. Therefore, such an intelligent data-driven approach ensures defect mitigation and stabilises the process by facilitating the advanced closed-loop decision-making in hybrid manufacturing environments. Higher accuracy ensures the model's capability for in situ process monitoring and defect identification. Moreover, the proposed framework also achieves superior performance compared to conventional ML approaches. Extensive robustness checks of this proposed CNN–LSTM framework are required to adopt and implement it for large-scale industrial applications.