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The Rapid Advancement Of Deep Learning In Natural Language Processing (NLP) Has Enabled Recurrent Neural Networks (RNNs), Particularly Long Short-Term Memory (LSTM) Architectures, To Generate Human-like Text Sequences. Despite Their Impressive Fluency, The Statistical Properties Of LSTM-generated Texts Often Diverge From Those Found In Natural Human Language. This Study Investigates The Statistical Features Of LSTM-generated Texts By Examining Linguistic Distributions, Such As Word Frequency, Sentence Length Variability, Entropy Measures, And Zipf’s Law Conformity. Comparative Analysis With Human-authored Corpora Highlights Areas Where LSTM Models Successfully Capture Natural Language Regularities And Where They Fall Short, Such As Long-range Dependencies And Higher-order Semantic Coherence. The Findings Provide Insights Into The Strengths And Limitations Of LSTM-based Text Generation, Offering A Deeper Understanding Of How Statistical Patterns Emerge In Synthetic Language. This Contributes To The Broader Evaluation Of Generative Models And Informs The Development Of More Linguistically Grounded NLP Systems.