In this study, a new strategy is presented to transmit the fundamental elastic beam problem into the
modern optimization platform and solve it by using artificial intelligence (AI) tools. As a practical example,
deflection of Euler-Bernoulli beam is mathematically formulated by 2nd-order ordinary differential equations
(ODEs) in accordance to the classical beam theory. This fundamental engineer problem is then transmitted from
classic formulation to its artificial-intelligence presentation where the behavior of the beam is simulated by using
neural networks (NNs). The supervised training strategy is employed in the developed NNs implemented in the
heuristic optimization algorithms as the fitness function. Different evolutionary optimization tools such as genetic
algorithm (GA) and particle swarm optimization (PSO) are used to solve this non-linear optimization problem. The
step-by-step procedure of the proposed method is presented in the form of a practical flowchart. The results indicate
that the proposed method of using AI tools in solving beam ODEs can efficiently lead to accurate solutions with low
computational costs, and should prove useful to solve more complex practical applications.