This study presents the generation and visualization of Non-Uniform Rational B-Spline surfaces derived from controlled curves, implemented in Python within the Jupyter Notebook environment. Three main approaches are examined: translation surfaces, revolution surfaces, and tensor product surfaces constructed from B-spline curves. Matrix-based algorithms and object-oriented implementations enable the precise modeling of parametric surfaces through control points, weights, and knot vectors. Among the approaches, revolution surfaces exhibit the highest geometric accuracy and smoothness when applied to symmetric base curves, such as circles and ellipses. Translation surfaces are computationally efficient but limited to linear or parallel curve interactions, while tensor product surfaces demonstrate greater versatility for complex free-form modeling, albeit with increased computational cost. Experimental results show that the methods achieve high visual fidelity and parametric continuity for both regular and irregular shapes, including cylinders, cones, hyperboloids, and user-defined geometries. In addition to its computational value, the study offers an educational perspective: the use of Python as a programming and visualization tool facilitates the comprehension of abstract mathematical concepts in undergraduate-level geometry and modeling courses. Its readable syntax and interactive environment foster experimentation, allowing students to engage with core ideas in computational geometry through practical implementations. These findings highlight the adaptability and pedagogical potential of NURBS surfaces in both scientific applications and mathematics education.