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In Software Engineering, The Clarity And Consistency Of Requirements Are Crucial For Successful Project Outcomes. However, Natural Language Requirements Are Often Ambiguous, Incomplete, Or Inconsistent, Leading To Costly Errors In Later Development Stages. To Address This, Requirements Templates Are Used To Standardize The Structure And Language Of Requirements. Despite Their Usefulness, Manually Verifying Conformance To These Templates Remains A Time-consuming And Error-prone Task. This Paper Presents An Automated Approach That Leverages Natural Language Processing (NLP) Techniques To Check The Conformance Of Requirements Documents Against Predefined Templates. The Proposed System Employs Syntactic And Semantic Analysis To Identify Structural Inconsistencies, Missing Elements, And Deviations From Standard Phrasing. By Integrating Machine Learning Models And Rule-based Methods, The System Can Adapt To Varying Template Styles And Domain-specific Vocabularies. Experimental Results On Real-world Datasets Demonstrate The Effectiveness Of The Approach In Improving Requirements Quality And Reducing The Manual Effort Required For Validation. This Work Contributes To Enhancing Automation In Requirements Engineering And Supports The Development Of More Reliable Software Systems.