This research addresses the challenge of predicting the probability of construction project progress by developing a comprehensive framework incorporating crucial factors. While previous studies have explored this issue using various methods and variables, a need remains for exploring it through different methodologies and elements. In this study, we utilise the multinomial logistic regression methodology to estimate progress probabilities based on finding essential variables via investigation of the levels of correlation between the progress project and some available factors in an available historical dataset. According to the level of relationship between the progress project and the number of involved companies, the numeric weight of each project type, the number and type of contracts per company, the project value and duration, it is founded the number of companies and duration as critical factors. The results of multinomial logistic regression modelling consider the number of companies and duration as independent variables and progress projects with different levels. Based on the research findings, the duration of a project and the number of businesses involved significantly impact the project’s progression. The probabilities of reaching specific advancement levels vary depending on the number of companies involved in projects with different durations. Shorter-duration projects are more likely to achieve lower advancement levels. In comparison, longer-duration projects with more businesses involved are more likely to reach higher levels of progress.