Why does Linear Regression objective remain convex?

Because MSE is a quadratic function in parameters, forming a convex surface.

The loss function is a sum of squared residuals, which expands into a quadratic form βᵀXᵀXβ - 2βᵀXᵀy. Since XᵀX is positive semi-definite, the function is convex, ensuring a global minimum.