In multiple linear regression analysis, the ordinary least squares (OLS) method has been the most popular technique for estimating parameters of linear regression model due to its optimal properties. OLS estimator may fail when the assumption of independence is violated. This assumption can be violated when there is correlation between the explanatory variables. Therefore, the data is said to contain multicollinearity and eventually will mislead the inferential statistics. When multicollinearity exists, biased estimation techniques are preferable to OLS. Many authors have proposed different estimators to overcome this problem. Also, many biased estimators with one-parameter or two-parameter are developed. But, the estimators with two-parameter have advantages over that with one-parameter where they have two biasing parameters and at least one of them has the property of handling this problem impact. Therefore, this study aims to examine the performance of seven recent estimators with two-parameter of multiple linear regression model with multicollinearity problem. The performance of the seven estimators, namely Owolabi et al. estimator (2022 a,b), Omara estimator (2022), Oladapo et al. estimator (2022), Makhdoom-Aslam estimator (2023), Idowu et al. estimator (2023), and Jassim-Alheety estimator (2023) are compared using Mean Square Error criterion. For this purpose, a simulation data with p = 3 , 8; n = 20 , 50 , 100; and full multicollinearity  = 0.7 , 0.8 , 0.9 , 0.99  was used. The existence of multicollinearity was evaluated usingVariance Inflation Factor (VIF) value. The empirical evidence shows that Jassim-Alheety estimator outperforms others under some conditions and is more efficient because it has the smallest MSE values in any samples sizes. A real-life dataset is used to demonstrate the findings of the paper. The comparison was made among the different models using both the mean square error (MSE) and mean absolute percentage error (MAPE), where the results agreed with the simulation results.