Surgical workflow analysis is crucial in optimising procedural efficiency, resource utilisation, and patient safety in catheterisation laboratories. Traditional manual work- flow analysis methods are labour-intensive and prone to inconsistencies, prompting the need for automated solutions that leverage machine learning and computer vision. This thesis presents an explainable two-stage model for surgical workflow analysis using ceiling-mounted cameras. The proposed approach integrates a YOLOv8 object detection model with a Gaussian Mixture Model - Hidden Markov Model (GMM-HMM) framework. The first stage detects key objects that serve as input to the second stage. The GMM-HMM component then infers surgical workflow phases by modelling spatial and temporal dynamics, enabling real-time phase classification. The model is validated on two datasets from different hospitals, achieving a classification accuracy of 95.2% for the RdGG dataset and 95.4% for the HH Tampere dataset, ensuring its generalisability across diverse clinical environments. Experimental results demonstrate that the model achieves high accuracy in detecting workflow phases, with an emphasis on explain- ability and robustness. The combination of YOLOv8’s efficient object detection with GMM-HMM’s structured temporal inference ensures that phase transitions are identified with minimal error. The model’s real-time feasibility and generalisation across hospitals highlight its potential for clinical implementation. This research advances automated surgical workflow analysis by addressing the dual challenges of interpretability and adaptability. Future work includes enhancing the model’s robustness to occlusions, integrating additional modalities such as audio data, and exploring its application in other surgical environments.