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In Major construction projects execution and performance were being negatively impacted by claims and disputes in terms of cost overrun, quality, stakeholders relationships, and productivity. Therefore understanding the significance of underlying the claims is essential. In this study, the primary root causes behind delay claims and disputes in construction projects were identified, examined, and rated. The significance of these factors was assessed using Relative Importance Index (RII) values. In addition, a machine learning model employing the Random Forest Genetic Algorithm (RFGA) was implemented to foresee the related risks and ascertain their levels. In a pilot survey, the data were collected across multiple construction projects at different phases such as scrutiny stage, design and planning stage, bidding stage, operation stage, and maintenance or after-construction stage. From Relative Important Index values from the statistical approach, it emerges that delay claims are generally causes from the owner followed by project-specific activities. Delays in processing bill payments, natural disasters, lack of contract awareness, and delay in final bill payment are the top causes of delay claims which converted to conflicts and disputes in mostly operating stage. The Random Forest Genetic Algorithm model predicted that factors like altering the original design, reluctance to cooperate by contractor, and increase of wages have lower risk whereas factors Poor site conditions, delay in approvals of schedules and change orders, natural calamities, late in running bill payment, repetition of work due to error in original work are at higher risk in terms of conflict and dispute. The model gives an accuracy of 0.89 and 0.87 for training data and testing data. The study will highlight possible research avenues and enhance project management strategies so that the project succeeds its goal.