Stochastic delay differential equations (SDDEs) are systems of differential equations with a time lag in a noisy or random environment. There are many nonlinear SDDEs where a linear growth condition is not satisfied, for example, the stochastic delay Lotka-Volterra model of food chain discussed by Xuerong Mao and Martina John Rassias in 2005. Much research has been done using discrete delay where the dynamics of a process at time t depend on the state of the process in the past after a single fixed time lag \tau. We are researching processes with continuously distributed delay which depend on weighted averages of past states over the entire time lag interval [t-\tau,t].By using martingale concepts, we prove sufficient conditions for the existence of a unique solution, ultimate boundedness, and non-extinction of one-dimensional nonlinear SDDE with continuously distributed delay. We give generalized Khasminskii-type conditions which along with local Lipschitz conditions are sufficient to guarantee the existence of a unique global solution of certain n-dimensional nonlinear SDDEs with continuously distributed delay. Further, we give conditions under which Euler-Maruyama numerical approximations of such nonlinear SDDEs converge in probability to their exact solutions.We give some examples of one-dimensional and 2-dimensional stochastic differential equations with continuously distributed delay which satisfy the sufficient conditions of our theorems. Moreover, we simulate their solutions and analyze the error of approximation using MATLAB to implement the Euler-Maruyama algorithm.