Exposure to natural hazards in the Sultanate of Oman is common in recent years due to the effects of the Sultanate's location, topography, climatic conditions, and urbanization activities. The negative effects of natural hazards are often exacerbated by their interactions with other hazards. Therefore, early planning and assessment of multiple hazards are of great importance to prevent, mitigate, and control these hazards. This study aims to estimate the risk from multiple hazards in the Governorate of Muscat considering all possible interactions between hazards using the MmhRisk HI quantitative model. The model is made up of five modules: hazard's identification, analysis, interaction analysis, exposure analysis, and vulnerability analysis. In hazard identification, Shannon's entropy model was used to identify the role of 15 factors in causing the hazards of coastal floods, flash-floods, and slope failures in each wilayat. According to the findings, the land-use factor plays a major role in causing coastal flooding in the Wilayats of Muscat and Qurayyat, while in the Wilayat of Seeb, the elevation factor plays a major role. Whereas the distance-to-road is the most contributor factor in the Wilayats of Bawshar and Muttrah, Considering the hazards of flash-floods and slope failures, the land-use factor plays an important role in causing these two hazards in the governorate. Accordingly, the spatial distribution of these three hazards in each wilayat was determined. In the hazard analysis, HEC-SSP v2.2 software was used to analyze the frequency and magnitude of hazards using rainfall records for the period between 1998 and 2016. The spatial interpolation method was then used to estimate the distribution of rainfall over the entire governorate. Based on the hazard's identification and analysis results, the probability of interaction between hazards was analyzed based on the relationship between them. In the exposure analysis, official census data was used to analyze the social's exposure to potential hazards using five elements: population density, percentage of the population aged ≤14 and ≥ 65 years, percentage of females, percentage of non-Omani, and percentage of people receiving government assistance. In the vulnerability analysis, the Bayesian Network (BN) model was used to estimate the percentage of the population likely to be affected by multiple hazards of different degrees. The analysis showed that the Wilayats of Bawshar and Seeb recorded the highest vulnerability at 9.1% and 8.6%, respectively, due to the higher proportion of non-Omanis, females, and the population aged ≤14 and ≥ 65 years residing in these wilayats compared to other wilayats in the governorate. Based on the results of the analysis for all modules, the percentage of population at risk from multiple hazards in each wilayat was estimated. According to the analysis, the Wilayats of Seeb and Bawshar showed the highest level of risk from multiple hazards with a probability of 3.2% and 2.7%, respectively. This is mainly due to their high level of vulnerability and the large number of residents living in these wilayats. The study, therefore. reveals the importance of the results and scientific information obtained for use by local authorities to implement appropriate measures in order to mitigate and improve preparedness for multiple hazards risks.