Studying the stability, steady-state behavior, and transient responses of stochastic delay differential equations presents significant numerical challenges. The Monte Carlo method, commonly used to estimate statistical properties, relies on averaging over numerous simulated trajectories. While straightforward, this approach is computationally intensive and often slow to converge, especially when estimating higher-order moments. We propose a more efficient and accurate alternative: a direct integration framework for the mean square dynamics of the system. The presented Mean Square Direct Integration (MSDI) method is based on the Euler-Maruyama scheme and directly captures the evolution of second-order moments, such as variance and standard deviation. This enables the assessment of fluctuations around the mean trajectory without computing an ensemble of sample paths. Numerical experiments demonstrate that MSDI achieves higher accuracy at significantly lower computational cost than traditional Monte Carlo simulations.