Movie recommendation models have become essential components of modern streaming services, influencing user engagement and happiness greatly. The success of a streaming service depends on its capacity to generate tailored movie recommendations. This paper makes a substantial impact on the field of recommendation models by presenting and in-depth analysing two different approaches to movie recommendations: a deep learning model utilizing Long Short-Term Memory (LSTM) networks and a content-based recommendation model using TF-IDF and Count Vectorization techniques with cosine similarity. The choice of recommendation approach effects both personalization and security. Whether LSTM models are used in recommendation systems or a content-based approach, security is a key concern. Strong authentication methods, encryption, and adherence to data protection regulations must all be included. We will evaluate these models using a real-world movie dataset, shedding light on their strengths and weaknesses and providing valuable insights for designing effective recommendation models. To enhance the precision and applicability of movie suggestions, this study uses Deep Learning, more especially Long Short-Term Memory (LSTM) networks. The proposed method only results in a 12% testing loss while achieving testing accuracy of 98%.